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Xu C, Li X, Zhang X, Wu R, Zhou Y, Zhao Q, Zhang Y, Geng S, Gu Y, Hong S. Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning. Health Inf Sci Syst 2024; 12:2. [PMID: 38045019 PMCID: PMC10692066 DOI: 10.1007/s13755-023-00249-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/20/2023] [Indexed: 12/05/2023] Open
Abstract
Cardiovascular disease (CVDs) has become one of the leading causes of death, posing a significant threat to human life. The development of reliable Artificial Intelligence (AI) assisted diagnosis algorithms for cardiac sounds is of great significance for early detection and treatment of CVDs. However, there is scarce research in this field. Existing research mainly faces three major challenges: (1) They mainly limited to murmur classification and cannot achieve murmur grading, but attempting both classification and grading may lead to negative effects between different multi-tasks. (2) They mostly pay attention to unstructured cardiac sound modality and do not consider the structured demographic modality, as it is difficult to balance the influence of heterogeneous modalities. (3) Deep learning methods lack interpretability, which makes it challenging to apply them clinically. To tackle these challenges, we propose a method for cardiac murmur grading and cardiac risk analysis based on heterogeneous modality adaptive multi-task learning. Specifically, a Hierarchical Multi-Task learning-based cardiac murmur detection and grading method (HMT) is proposed to prevent negative interference between different tasks. In addition, a cardiac risk analysis method based on Heterogeneous Multi-modal feature impact Adaptation (HMA) is also proposed, which transforms unstructured modality into structured modality representation, and utilizes an adaptive mode weight learning mechanism to balance the impact between unstructured modality and structured modality, thus enhancing the performance of cardiac risk prediction. Finally, we propose a multi-task interpretability learning module that incorporates an important evaluation using random masks. This module utilizes SHAP graphs to visualize crucial murmur segments in cardiac sound and employs a multi-factor risk decoupling model based on nomograms. And then we gain insights into the cardiac disease risk in both pre-decoupled multi-modality and post-decoupled single-modality scenarios, thus providing a solid foundation for AI assisted cardiac murmur grading and risk analysis. Experimental results on a large real-world CirCor DigiScope PCG dataset demonstrate that the proposed method outperforms the state-of-the-art (SOTA) method in murmur detection, grading, and cardiac risk analysis, while also providing valuable diagnostic evidence.
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Affiliation(s)
- Chenyang Xu
- Department of Computer Science, Tianjin University of Technology, Tianjin, China
| | - Xin Li
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Xinyue Zhang
- Department of Computer Science, Tianjin University of Technology, Tianjin, China
| | - Ruilin Wu
- Department of Computer Science, Tianjin University of Technology, Tianjin, China
| | - Yuxi Zhou
- Department of Computer Science, Tianjin University of Technology, Tianjin, China
- DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China
| | - Qinghao Zhao
- Department of Cardiology, Peking University People’s Hospital, Beijing, China
| | - Yong Zhang
- DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China
| | | | - Yue Gu
- Department of Computer Science, Tianjin University of Technology, Tianjin, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University, Beijing, China
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2
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Xie J, Mothe B, Alcalde Herraiz M, Li C, Xu Y, Jödicke AM, Gao Y, Wang Y, Feng S, Wei J, Chen Z, Hong S, Wu Y, Su B, Zheng X, Cohet C, Ali R, Wareham N, Alhambra DP. Relationship between HLA genetic variations, COVID-19 vaccine antibody response, and risk of breakthrough outcomes. Nat Commun 2024; 15:4031. [PMID: 38740772 DOI: 10.1038/s41467-024-48339-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
The rapid global distribution of COVID-19 vaccines, with over a billion doses administered, has been unprecedented. However, in comparison to most identified clinical determinants, the implications of individual genetic factors on antibody responses post-COVID-19 vaccination for breakthrough outcomes remain elusive. Here, we conducted a population-based study including 357,806 vaccinated participants with high-resolution HLA genotyping data, and a subset of 175,000 with antibody serology test results. We confirmed prior findings that single nucleotide polymorphisms associated with antibody response are predominantly located in the Major Histocompatibility Complex region, with the expansive HLA-DQB1*06 gene alleles linked to improved antibody responses. However, our results did not support the claim that this mutation alone can significantly reduce COVID-19 risk in the general population. In addition, we discovered and validated six HLA alleles (A*03:01, C*16:01, DQA1*01:02, DQA1*01:01, DRB3*01:01, and DPB1*10:01) that independently influence antibody responses and demonstrated a combined effect across HLA genes on the risk of breakthrough COVID-19 outcomes. Lastly, we estimated that COVID-19 vaccine-induced antibody positivity provides approximately 20% protection against infection and 50% protection against severity. These findings have immediate implications for functional studies on HLA molecules and can inform future personalised vaccination strategies.
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Affiliation(s)
- Junqing Xie
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Beatriz Mothe
- Infectious Diseases Department, IrsiCaixa AIDS Research Institute, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
| | - Marta Alcalde Herraiz
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Chunxiao Li
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Annika M Jödicke
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Yaqing Gao
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Yunhe Wang
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK
| | - Shuo Feng
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK
| | - Jia Wei
- Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
| | - Zhuoyao Chen
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Binbin Su
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Xiaoying Zheng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Catherine Cohet
- Real-World Evidence Workstream, Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, Noord-Holland, The Netherlands
| | - Raghib Ali
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
- Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Nick Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Daniel Prieto Alhambra
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK.
- Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, The Netherlands.
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3
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Guo RX, Tian X, Bazoukis G, Tse G, Hong S, Chen KY, Liu T. Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia. Pacing Clin Electrophysiol 2024. [PMID: 38712484 DOI: 10.1111/pace.14995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 03/29/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024]
Abstract
The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.
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Affiliation(s)
- Rong-Xin Guo
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xu Tian
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Inomenon Polition Amerikis, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Gary Tse
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
- Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
- School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Kang-Yin Chen
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Tong Liu
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
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4
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Gao Y, Su B, Ding L, Qureshi D, Hong S, Wei J, Zeng C, Lei G, Xie J. Association of Regular Opioid Use With Incident Dementia and Neuroimaging Markers of Brain Health in Chronic Pain Patients: Analysis of UK Biobank. Am J Geriatr Psychiatry 2024:S1064-7481(24)00320-8. [PMID: 38702251 DOI: 10.1016/j.jagp.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVES We aimed to investigate the association of regular opioid use, compared with non-opioid analgesics, with incident dementia and neuroimaging outcomes among chronic pain patients. DESIGN The primary design is a prospective cohort study. To triangulate evidence, we also conducted a nested case-control study analyzing opioid prescriptions and a cross-sectional study analyzing neuroimaging outcomes. SETTING AND PARTICIPANTS Dementia-free UK Biobank participants with chronic pain and regular analgesic use. MEASUREMENTS Chronic pain status and regular analgesic use were captured using self-reported questionnaires and verbal interviews. Opioid prescription data were obtained from primary care records. Dementia cases were ascertained using primary care, hospital, and death registry records. Propensity score-matched Cox proportional hazards analysis, conditional logistic regression, and linear regression were applied to the data in the prospective cohort, nested case-control, and cross-sectional studies, respectively. RESULTS Prospective analyses revealed that regular opioid use, compared with non-opioid analgesics, was associated with an increased dementia risk over the 15-year follow-up (Hazard ratio [HR], 1.18 [95% confidence interval (CI): 1.08-1.30]; Absolute rate difference [ARD], 0.44 [95% CI: 0.19-0.71] per 1000 person-years; Wald χ2 = 3.65; df = 1; p <0.001). The nested case-control study suggested that a higher number of opioid prescriptions was associated with an increased risk of dementia (1 to 5 prescriptions: OR = 1.21, 95% CI: 1.07-1.37, Wald χ2 = 3.02, df = 1, p = 0.003; 6 to 20: OR = 1.27, 95% CI: 1.08-1.50, Wald χ2 = 2.93, df = 1, p = 0.003; more than 20: OR = 1.43, 95% CI: 1.23-1.67, Wald χ2 = 4.57, df = 1, p < 0.001). Finally, neuroimaging analyses revealed that regular opioid use was associated with lower total grey matter and hippocampal volumes, and higher white matter hyperintensities volumes. CONCLUSION Regular opioid use in chronic pain patients was associated with an increased risk of dementia and poorer brain health when compared to non-opioid analgesic use. These findings imply a need for re-evaluation of opioid prescription practices for chronic pain patients and, if further evidence supports causality, provide insights into strategies to mitigate the burden of dementia.
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Affiliation(s)
- Yaqing Gao
- Nuffield Department of Population Health (YG, DQ), University of Oxford, Oxford, UK
| | - Binbin Su
- School of Population Medicine and Public Health (BS), Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Lei Ding
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (LD), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Danial Qureshi
- Nuffield Department of Population Health (YG, DQ), University of Oxford, Oxford, UK
| | - Shenda Hong
- National Institute of Health Data Science (SH), Peking University, Beijing, China; Institute of Medical Technology (SH), Peking University Health Science Center, Beijing, China
| | - Jie Wei
- Department of Orthopaedics (JW, CZ, GL), Xiangya Hospital, Central South University, Changsha, China
| | - Chao Zeng
- Department of Orthopaedics (JW, CZ, GL), Xiangya Hospital, Central South University, Changsha, China
| | - Guanghua Lei
- Department of Orthopaedics (JW, CZ, GL), Xiangya Hospital, Central South University, Changsha, China.
| | - Junqing Xie
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS (JX), University of Oxford, Oxford, UK.
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5
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Rosenberg E, Andersen TI, Samajdar R, Petukhov A, Hoke JC, Abanin D, Bengtsson A, Drozdov IK, Erickson C, Klimov PV, Mi X, Morvan A, Neeley M, Neill C, Acharya R, Allen R, Anderson K, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bilmes A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Campero J, Chang HS, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Barba ADT, Demura S, Di Paolo A, Dunsworth A, Earle C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Garcia G, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hill G, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Mandrà S, Martin O, Martin S, McClean JR, McEwen M, Meeks S, Miao KC, Mieszala A, Montazeri S, Movassagh R, Mruczkiewicz W, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Omonije S, Opremcak A, Potter R, Pryadko LP, Quintana C, Rhodes DM, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Sivak V, Skruzny J, Smith WC, Somma RD, Sterling G, Strain D, Szalay M, Thor D, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Khemani V, Gopalakrishnan S, Prosen T, Roushan P. Dynamics of magnetization at infinite temperature in a Heisenberg spin chain. Science 2024; 384:48-53. [PMID: 38574139 DOI: 10.1126/science.adi7877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Affiliation(s)
- E Rosenberg
- Google Research, Mountain View, CA, USA
- Department of Physics, Cornell University, Ithaca, NY, USA
| | | | - R Samajdar
- Department of Physics, Princeton University, Princeton, NJ, USA
- Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, USA
| | | | - J C Hoke
- Department of Physics, Stanford University, Stanford, CA, USA
| | - D Abanin
- Google Research, Mountain View, CA, USA
| | | | - I K Drozdov
- Google Research, Mountain View, CA, USA
- Department of Physics, University of Connecticut, Storrs, CT, USA
| | | | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - A Morvan
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - R Allen
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - A Bilmes
- Google Research, Mountain View, CA, USA
| | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - J Campero
- Google Research, Mountain View, CA, USA
| | - H-S Chang
- Google Research, Mountain View, CA, USA
| | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | - C Earle
- Google Research, Mountain View, CA, USA
| | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - G Garcia
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | - G Hill
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- QSI, Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - S Mandrà
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | - S Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | - S Meeks
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - S Omonije
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - V Sivak
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | | | - R D Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - D Thor
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | | | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - V Khemani
- Department of Physics, Stanford University, Stanford, CA, USA
| | | | - T Prosen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - P Roushan
- Google Research, Mountain View, CA, USA
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6
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Zhou T, Shen Y, Lyu J, Yang L, Wang HJ, Hong S, Ji Y. Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study. Healthcare (Basel) 2024; 12:713. [PMID: 38610136 PMCID: PMC11011488 DOI: 10.3390/healthcare12070713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/16/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Early identification of children with neurodevelopmental abnormality is a major challenge, which is crucial for improving symptoms and preventing further decline in children with neurodevelopmental abnormality. This study focuses on developing a predictive model with maternal sociodemographic, behavioral, and medication-usage information during pregnancy to identify infants with abnormal neurodevelopment before the age of one. In addition, an interpretable machine-learning approach was utilized to assess the importance of the variables in the model. In this study, artificial neural network models were developed for the neurodevelopment of five areas of infants during the first year of life and achieved good predictive efficacy in the areas of fine motor and problem solving, with median AUC = 0.670 (IQR: 0.594, 0.764) and median AUC = 0.643 (IQR: 0.550, 0.731), respectively. The final model for neurodevelopmental abnormalities in any energy region of one-year-old children also achieved good prediction performance. The sensitivity is 0.700 (IQR: 0.597, 0.797), the AUC is 0.821 (IQR: 0.716, 0.833), the accuracy is 0.721 (IQR: 0.696, 0.739), and the specificity is 0.742 (IQR: 0.680, 0.748). In addition, interpretable machine-learning methods suggest that maternal exposure to drugs such as acetaminophen, ferrous succinate, and midazolam during pregnancy affects the development of specific areas of the offspring during the first year of life. This study established predictive models of neurodevelopmental abnormality in infants under one year and underscored the prediction value of medication exposure during pregnancy for the neurodevelopmental outcomes of the offspring.
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Affiliation(s)
- Tianyi Zhou
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China; (T.Z.); (Y.S.); (J.L.); (H.-J.W.)
- Peking University Health Science Center-Weifang Joint Research Center for Maternal and Child Health, Beijing 100191, China
| | - Yaojia Shen
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China; (T.Z.); (Y.S.); (J.L.); (H.-J.W.)
- Peking University Health Science Center-Weifang Joint Research Center for Maternal and Child Health, Beijing 100191, China
| | - Jinlang Lyu
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China; (T.Z.); (Y.S.); (J.L.); (H.-J.W.)
- Peking University Health Science Center-Weifang Joint Research Center for Maternal and Child Health, Beijing 100191, China
| | - Li Yang
- Tongzhou Maternal and Child Health Care Hospital of Beijing, Beijing 101101, China;
| | - Hai-Jun Wang
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China; (T.Z.); (Y.S.); (J.L.); (H.-J.W.)
- Peking University Health Science Center-Weifang Joint Research Center for Maternal and Child Health, Beijing 100191, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing 100191, China;
| | - Yuelong Ji
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China; (T.Z.); (Y.S.); (J.L.); (H.-J.W.)
- Peking University Health Science Center-Weifang Joint Research Center for Maternal and Child Health, Beijing 100191, China
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7
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Mi X, Michailidis AA, Shabani S, Miao KC, Klimov PV, Lloyd J, Rosenberg E, Acharya R, Aleiner I, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Malone FD, Martin O, McClean JR, McEwen M, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Roushan P, Smelyanskiy V, Abanin DA. Stable quantum-correlated many-body states through engineered dissipation. Science 2024; 383:1332-1337. [PMID: 38513021 DOI: 10.1126/science.adh9932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 02/13/2024] [Indexed: 03/23/2024]
Abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Affiliation(s)
- X Mi
- Google Research, Mountain View, CA, USA
| | - A A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - S Shabani
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | - J Lloyd
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | | | - R Acharya
- Google Research, Mountain View, CA, USA
| | - I Aleiner
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - C Chou
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | - A G Dau
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | | | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Software and Information (QSI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | | | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | | | | | - A Morvan
- Google Research, Mountain View, CA, USA
| | | | | | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | | | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Google Research, Mountain View, CA, USA
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - R Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z J Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | - P Roushan
- Google Research, Mountain View, CA, USA
| | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
- Department of Physics, Princeton University, Princeton, NJ, USA
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8
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Gao Y, Su B, Luo Y, Tian Y, Hong S, Gao S, Xie J, Zheng X. HLA-C*07:01 and HLA-DQB1*02:01 protect against white matter hyperintensities and deterioration of cognitive function: A population-based cohort study. Brain Behav Immun 2024; 115:250-257. [PMID: 37884160 DOI: 10.1016/j.bbi.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/14/2023] [Accepted: 10/22/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Neuroinflammation and aberrant immune regulation are increasingly implicated in the pathophysiology of white matter hyperintensities (WMH), an imaging marker of cerebrovascular pathologies and predictor of cognitive impairment. The role of human leukocyte antigen (HLA) genes, critical in immunoregulation and associated with susceptibility to neurodegenerative diseases, in WMH pathophysiology remains unexplored. METHODS We performed association analyses between classical HLA alleles and WMH volume, derived from MRI scans of 38 302 participants in the UK Biobank. To identify independent functional alleles driving these associations, we conducted conditional forward stepwise regression and lasso regression. We further investigated whether these functional alleles showed consistent associations with WMH across subgroups characterized by varying levels of clinical determinants. Additionally, we validated the clinical relevance of the identified alleles by examining their association with cognitive function (n = 147 549) and dementia (n = 460 029) in a larger cohort. FINDINGS Four HLA alleles (DQB1*02:01, DRB1*03:01, C*07:01, and B*08:01) showed an association with reduced WMH volume after Bonferroni correction for multiple comparisons. Among these alleles, DQB1*02:01 exhibited the most significant association (β = -0.041, 95 % CI: -0.060 to -0.023, p = 1.04 × 10-5). Forward selection and lasso regression analyses indicated that DQB1*02:01 and C*07:01 primarily drove this association. The protective effect against WMH conferred by DQB1*02:01 and C*07:01 persisted in clinically relevant subgroups, with a stronger effect observed in older participants. Carrying DQB1*02:01 and C*07:01 was associated with higher cognitive function, but no association with dementia was found. INTERPRETATION Our population-based findings support the involvement of immune-associated mechanisms, particularly both HLA class I and class II genes, in the pathogenesis of WMH and subsequent consequence of cognitive functions.
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Affiliation(s)
- Yaqing Gao
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Binbin Su
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Yanan Luo
- Department of Global Health, School of Public Health, Peking University, Beijing, China
| | - Yaohua Tian
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China; Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Song Gao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Junqing Xie
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK.
| | - Xiaoying Zheng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China; HeSAY, Peking University, Beijing, China.
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9
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Hong S, Zhao Q. Expanding electrocardiogram abilities for postoperative mortality prediction with deep learning. Lancet Digit Health 2024; 6:e4-e5. [PMID: 38065779 DOI: 10.1016/s2589-7500(23)00230-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/09/2023] [Indexed: 12/22/2023]
Affiliation(s)
- Shenda Hong
- National Institute of Health Data Science and Institute of Medical Technology, Health Science Center, Peking University, Beijing 100191, China.
| | - Qinghao Zhao
- Department of Cardiology, Peking University People's Hospital, Beijing, China
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10
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Wang J, Zhang C, Xu L, Wei J, Yang J, Strohl AE, Yi H, Liu X, Zhang L, Zhao R, Hong S, Zhou B, Zhao L, Zhang X, Dong X, Strohl KP, Wang J, Liu G, Han F. Home monitoring for clinically suspected obstructive sleep apnea in pregnancy. J Clin Sleep Med 2023; 19:1951-1960. [PMID: 37485700 PMCID: PMC10620654 DOI: 10.5664/jcsm.10726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/18/2023] [Accepted: 07/18/2023] [Indexed: 07/25/2023]
Abstract
STUDY OBJECTIVES To determine if a home sleep apnea test (HSAT) using a type III portable monitor (PM), Nox-T3 (Nox Medical, Inc., Reykjavik, Iceland), detects obstructive sleep apnea in pregnant women. METHODS Ninety-two pregnant women (34.5 ± 4.3 years; gestational age 25.4 ± 8.9 weeks; body mass index 29.9 ± 4.7 kg/m2) with suspected obstructive sleep apnea underwent HSAT with the Nox-T3 PM followed by overnight polysomnography (PSG) and PM recording simultaneously in the laboratory within 1 week. PMs were scored automatically and manually using a 3% criteria and compared with PSGs scored by following guidelines. RESULTS Apnea-hypopnea indexes were 8.56 ± 10.42, 8.19 ± 13.79, and 8.71 ± 14.19 events/h on HSAT, in-laboratory PM recording, and PSG (P = .955), respectively. Bland-Altman analysis of the apnea-hypopnea index on PSG vs HSAT showed a mean difference (95% confidence interval) of -0.15 (-1.83, 1.53); limits of agreement (± 2 SD) were -16.26 to 16.56 events/h. Based on a threshold apnea-hypopnea index ≥ 5 events/h, HSAT had 91% sensitivity, 85% specificity, 84% positive-predictive value, and 92% negative-predictive value compared with PSG. When comparing the simultaneous recordings, closer agreement was observed. Automated vs manual analysis of PM showed no significant difference. CONCLUSIONS A type III PM had an acceptable failure rate and high diagnostic performance operating as a reasonable alternative for in-laboratory PSG in pregnant women. CITATION Wang J, Zhang C, Xu L, et al. Home monitoring for clinically suspected obstructive sleep apnea in pregnancy. J Clin Sleep Med. 2023;19(11):1951-1960.
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Affiliation(s)
- Jingyu Wang
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
| | - Chi Zhang
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
| | - Liyue Xu
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
| | - Jun Wei
- Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing, China
| | - Jingjing Yang
- Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing, China
| | - Anna E. Strohl
- Department of OB/GYN-Gynecological Oncology, University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland Ohio
| | - Huijie Yi
- Peking University School of Nursing, Beijing, China
| | - Xinran Liu
- Peking University School of Nursing, Beijing, China
| | - Linyan Zhang
- Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing, China
| | - Rui Zhao
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Bing Zhou
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
| | - Long Zhao
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
| | - Xueli Zhang
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
| | - Xiaosong Dong
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
| | - Kingman P. Strohl
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Case Western Reserve University, and Cleveland Louis Stokes VA Medical Center, Cleveland, Ohio
| | - Jianliu Wang
- Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing, China
| | - Guoli Liu
- Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing, China
| | - Fang Han
- Division of Sleep Medicine, Peking University People’s Hospital, Beijing, China
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11
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Ward MC, Atlas JL, Carrizosa DR, Milas ZL, Brickman DS, Frenkel CH, Hong S, Heinzerling JH, Prabhu RS, Moeller BJ. Weekly vs. Bolus Cisplatin Concurrent with Definitive Radiotherapy for Squamous Carcinoma of the Head and Neck: A Systematic Review and Network Meta-Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e632-e633. [PMID: 37785889 DOI: 10.1016/j.ijrobp.2023.06.2031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The optimal schedule for cisplatin delivered concurrently with definitive radiation for squamous carcinoma of the head and neck remains controversial. Randomized data in the postoperative setting is mixed, and definitive studies are ongoing. Meanwhile, multiple trials have already compared cetuximab to cisplatin in the definitive setting. Across these trials, the cetuximab dosing was identical, but cisplatin dosing was variable and can be categorized as weekly (40 mg/m2 q1 week) or bolus (100 mg/m2 q3 weeks). We indirectly compared these two cisplatin schedules by performing a network meta-analysis of cetuximab trials. MATERIALS/METHODS We performed a PRISMA-concordant systematic review to identify randomized controlled trials comparing cisplatin to cetuximab for patients with non-metastatic squamous carcinoma of the head and neck treated with definitive radiation therapy. Trials of primary surgery, incorporating induction therapy, or mixing other therapeutics were excluded. The analysis was pre-registered with the Open Science Foundation. Individual patient survival data was extracted from the published overall survival curves using a digitizer, and outcomes were validated against published point-estimates and hazard ratios. A random effects Cox regression was used to perform the individual-patient analysis using a one-step approach under a frequentist framework. Random effects were applied to model heterogeneity in the baseline hazard function and treatment effect. Models were adjusted by HPV and smoking status, which were trial-level covariates. Alternative endpoints (DFS, LRF, DM, etc.) were analyzed qualitatively. IRB approval was not required. RESULTS Five randomized trials were identified, including 1,678 patients. Bolus cisplatin was delivered to 572 patients in 2 trials, and weekly to 271 in 3 trials. The risk of bias was low. Relative to cetuximab, both bolus and weekly cisplatin reduced the risk of death (adjusted HR 0.63, 95% CI 0.46-0.87, p = 0.004 & HR 0.56, 95% CI 0.37-0.86, p = 0.008 respectively). No interaction was identified between regimen and HPV or smoking status. Between-study heterogeneity (δ2) was 0.148 and treatment effect heterogeneity (τ2) was small (<0.0002). There was no statistical difference in OS between bolus vs. weekly regimens (weekly vs. bolus HR 0.90, 95% CI 0.53-1.52, p = 0.345). This Cox model therefore suggested that on average, the absolute difference in 5-year OS is <1.5% between the two regimens, which was not statistically significant. Secondary endpoints and toxicity were not obviously different by regimen, qualitatively. CONCLUSION Using cetuximab as a common reference, there was no significant difference in survival between weekly and bolus cisplatin schedules.
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Affiliation(s)
- M C Ward
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - J L Atlas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D R Carrizosa
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - Z L Milas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D S Brickman
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - C H Frenkel
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - S Hong
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - J H Heinzerling
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - R S Prabhu
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - B J Moeller
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
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Hoke JC, Ippoliti M, Rosenberg E, Abanin D, Acharya R, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Eppens D, Erickson C, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Miao KC, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Mi X, Khemani V, Roushan P. Measurement-induced entanglement and teleportation on a noisy quantum processor. Nature 2023; 622:481-486. [PMID: 37853150 PMCID: PMC10584681 DOI: 10.1038/s41586-023-06505-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/01/2023] [Indexed: 10/20/2023]
Abstract
Measurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the 'arrow of time' that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time3-10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11-13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15-17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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Sud S, Poellmann M, Garg V, King T, Casey DL, Wang AZ, Hong S, Weiner AA. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Localized Lung Cancer Treated with Radiotherapy or Chemoradiotherapy with Definitive Intent. Int J Radiat Oncol Biol Phys 2023; 117:e60. [PMID: 37785811 DOI: 10.1016/j.ijrobp.2023.06.778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To characterize circulating tumor cell (CTC) kinetics in response to definitive therapy in patients with local or locoregional lung cancer and identify CTC kinetic profiles associated with favorable disease response versus progression. MATERIALS/METHODS In this single-institution prospective correlative biomarker study, we enrolled patients receiving definitive intent radiotherapy (RT) or chemoradiotherapy for non-metastatic lung cancer. Blood specimens were collected prior to RT (baseline), during RT and at follow up visits up to 24 months post RT. Subsequent lines of therapy were administered per standard of care. CTCs were captured and enumerated using a previously reported nanotechnology-based assay functionalized with aEpCAM, aHER-2, and aEGFR to facilitate biomimetic cell rolling and dendrimer-mediated multivalent binding. Disease status was assessed per RECIST 1.1 criteria. CTC kinetics and absolute values were analyzed to identify patterns associated with disease control versus progression. RESULTS We enrolled 24 patients with median follow up of 8 months corresponding to 114 CTC measurements. Seven patients (30%) had biopsy proven disease, while 17 (70%) were diagnosed based on clinical and radiographic features alone. Nineteen patients (79%) received stereotactic body radiation therapy. Median baseline CTC count was 12.6 CTCs/ml (range 0-290) and post RT decreased to median 4 CTCs/ml (0-42.7). For 95% of patients, a favorable kinetic profile (defined as stable CTC count, decreased CTC count or <24 CTCs/ml corresponding to the 80th percentile) during radiotherapy or at the time of first follow up corresponded to local control of the irradiated lesion. Five patients (20%) experienced disease progression within the follow up period. In the two patients with local progression of the irradiated lesion, the CTC count rose >10 fold prior to or at the time of radiographic detection of progression. In the three patients with systemic progression, CTC count rose 1.46-5.8-fold at the time of progression. Notably, four of the five patients with disease progression did not have initial biopsy confirmation of disease but did experience a CTC elevation at the time of progression. CONCLUSION Our data suggests CTCs may serve as a biomarker for response to therapy in patients being treated with RT with definitive intent for early stage or locally advanced lung cancer. This finding is of importance given important limitations in obtaining pathologic confirmation of disease in select patients and challenges distinguishing disease progression versus benign post radiotherapy radiographic changes. Further studies are needed to characterize the predictive and prognostic value of circulating biomarker levels and kinetics in lung cancer.
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Affiliation(s)
- S Sud
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - M Poellmann
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - V Garg
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - T King
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - D L Casey
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - A Z Wang
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC; UT Southwestern Department of Radiation Oncology, Dallas, TX
| | - S Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - A A Weiner
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
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Zhang W, Yang L, Geng S, Hong S. Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer. IEEE Trans Neural Netw Learn Syst 2023; PP:1-10. [PMID: 37478042 DOI: 10.1109/tnnls.2023.3292066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Since labeled samples are typically scarce in real-world scenarios, self-supervised representation learning in time series is critical. Existing approaches mainly employ the contrastive learning framework, which automatically learns to understand similar and dissimilar data pairs. However, they are constrained by the request for cumbersome sampling policies and prior knowledge of constructing pairs. Also, few works have focused on effectively modeling temporal-spectral correlations to improve the capacity of representations. In this article, we propose the cross reconstruction transformer (CRT) to solve the aforementioned issues. CRT achieves time series representation learning through a cross-domain dropping-reconstruction task. Specifically, we obtain the frequency domain of the time series via the fast Fourier transform (FFT) and randomly drop certain patches in both time and frequency domains. Dropping is employed to maximally preserve the global context while masking leads to the distribution shift. Then a Transformer architecture is utilized to adequately discover the cross-domain correlations between temporal and spectral information through reconstructing data in both domains, which is called Dropped Temporal-Spectral Modeling. To discriminate the representations in global latent space, we propose instance discrimination constraint (IDC) to reduce the mutual information between different time series samples and sharpen the decision boundaries. Additionally, a specified curriculum learning (CL) strategy is employed to improve the robustness during the pretraining phase, which progressively increases the dropping ratio in the training process. We conduct extensive experiments to evaluate the effectiveness of the proposed method on multiple real-world datasets. Results show that CRT consistently achieves the best performance over existing methods by 2%-9%. The code is publicly available at https://github.com/BobZwr/Cross-Reconstruction-Transformer.
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Wang Z, Yang H, Sun C, Hong S. Estimating causal effects of physical disability and number of comorbid chronic diseases on risk of depressive symptoms in an elderly Chinese population: a machine learning analysis of cross-sectional baseline data from the China longitudinal ageing social survey. BMJ Open 2023; 13:e069298. [PMID: 37407052 DOI: 10.1136/bmjopen-2022-069298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/07/2023] Open
Abstract
OBJECTIVE This study aimed to explore the causal effects of physical disability and number of comorbid chronic diseases on depressive symptoms in an elderly Chinese population. DESIGN, SETTING AND ANALYSIS Cross-sectional, baseline data were obtained from the China Longitudinal Ageing Social Survey, a stratified, multistage, probabilistic sampling survey conducted in 2014 that covers 28 of 31 provincial areas in China. The causal effects of physical disability and number of comorbid chronic diseases on depressive symptoms were analysed using the conditional average treatment effect method of machine learning. The causal effects model's adjustment was made for age, gender, residence, marital status, educational level, ethnicity, wealth quantile and other factors. OUTCOME Assessment of the causal effects of physical disability and number of comorbid chronic diseases on depressive symptoms. PARTICIPANTS 7496 subjects who were 60 years of age or older and who answered the questions on depressive symptoms and other independent variables of interest in a survey conducted in 2014 were included in this study. RESULTS Physical disability and number of comorbid chronic diseases had causal effects on depressive symptoms. Among the subjects who had one or more functional limitations, the probability of depressive symptoms increased by 22% (95% CI 19% to 24%). For the subjects who had one chronic disease and those who had two or more chronic diseases, the possibility of depressive symptoms increased by 13% (95% CI 10% to 15%) and 20% (95% CI 18% to 22%), respectively. CONCLUSION This study provides evidence that the presence of one or more functional limitations affects the occurrence of depressive symptoms among elderly people. The findings of our study are of value in developing programmes that are designed to identify elderly individuals who have physical disabilities or comorbid chronic diseases to provide early intervention.
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Affiliation(s)
- Zhenjie Wang
- Institute of Population Research, Peking University, Beijing, People's Republic of China
| | - Hanmo Yang
- T. H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Chenxi Sun
- School of Intelligence Science and Technology and the Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, People's Republic of China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, People's Republic of China
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Smitherman EA, Chahine RA, Beukelman T, Lewandowski LB, Rahman AKMF, Wenderfer SE, Curtis JR, Hersh AO, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar‐Smiley F, Barillas‐Arias L, Basiaga M, Baszis K, Becker M, Bell‐Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang‐Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel‐Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie‐Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui‐Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein‐Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PM, McGuire S, McHale I, McMonagle A, McMullen‐Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O'Brien B, O'Brien T, Okeke O, Oliver M, Olson J, O'Neil K, Onel K, Orandi A, Orlando M, Osei‐Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan‐Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas‐Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth‐Wojcicki E, Rouster – Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert‐Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner‐Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Childhood-Onset Lupus Nephritis in the Childhood Arthritis and Rheumatology Research Alliance Registry: Short-Term Kidney Status and Variation in Care. Arthritis Care Res (Hoboken) 2023; 75:1553-1562. [PMID: 36775844 PMCID: PMC10500561 DOI: 10.1002/acr.25002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 07/14/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The goal was to characterize short-term kidney status and describe variation in early care utilization in a multicenter cohort of patients with childhood-onset systemic lupus erythematosus (cSLE) and nephritis. METHODS We analyzed previously collected prospective data from North American patients with cSLE with kidney biopsy-proven nephritis enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry from March 2017 through December 2019. We determined the proportion of patients with abnormal kidney status at the most recent registry visit and applied generalized linear mixed models to identify associated factors. We also calculated frequency of medication use, both during induction and ever recorded. RESULTS We identified 222 patients with kidney biopsy-proven nephritis, with 64% class III/IV nephritis on initial biopsy. At the most recent registry visit at median (interquartile range) of 17 (8-29) months from initial kidney biopsy, 58 of 106 patients (55%) with available data had abnormal kidney status. This finding was associated with male sex (odds ratio [OR] 3.88, 95% confidence interval [95% CI] 1.21-12.46) and age at cSLE diagnosis (OR 1.23, 95% CI 1.01-1.49). Patients with class IV nephritis were more likely than class III to receive cyclophosphamide and rituximab during induction. There was substantial variation in mycophenolate, cyclophosphamide, and rituximab ever use patterns across rheumatology centers. CONCLUSION In this cohort with predominately class III/IV nephritis, male sex and older age at cSLE diagnosis were associated with abnormal short-term kidney status. We also observed substantial variation in contemporary medication use for pediatric lupus nephritis between pediatric rheumatology centers. Additional studies are needed to better understand the impact of this variation on long-term kidney outcomes.
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Hong H, Hong S. simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models. Health Data Sci 2023; 3:0023. [PMID: 38487195 PMCID: PMC10880161 DOI: 10.34133/hds.0023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 03/17/2024]
Abstract
Background Logistic regression models are widely used in clinical prediction, but their application in resource-poor settings or areas without internet access can be challenging. Nomograms can serve as a useful visualization tool to speed up the calculation procedure, but existing nomogram generators often require the input of raw data, inhibiting the transformation of established logistic regression models that only provide coefficients. Developing a tool that can generate nomograms directly from logistic regression coefficients would greatly increase usability and facilitate the translation of research findings into patient care. Methods We designed and developed simpleNomo, an open-source Python toolbox that enables the construction of nomograms for logistic regression models. Uniquely, simpleNomo allows for the creation of nomograms using only the coefficients of the model. Further, we also devoloped an online website for nomogram generation. Results simpleNomo properly maintains the predictive ability of the original logistic regression model and easy to follow. simpleNomo is compatible with Python 3 and can be installed through Python Package Index (PyPI) or https://github.com/Hhy096/nomogram. Conclusion This paper presents simpleNomo, an open-source Python toolbox for generating nomograms for logistic regression models. It facilitates the process of transferring established logistic regression models to nomograms and can further convert more existing works into practical use.
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Affiliation(s)
- Haoyang Hong
- National Institute of Health Data Science,
Peking University, Beijing, China
- School of Data Science,
Chinese University of Hong Kong, Shenzhen, China
| | - Shenda Hong
- National Institute of Health Data Science,
Peking University, Beijing, China
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Andersen TI, Lensky YD, Kechedzhi K, Drozdov IK, Bengtsson A, Hong S, Morvan A, Mi X, Opremcak A, Acharya R, Allen R, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Babbush R, Bacon D, Bardin JC, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hilton J, Hoffmann MR, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Lucero E, Malone FD, Martin O, McClean JR, McCourt T, McEwen M, Miao KC, Mieszala A, Mohseni M, Montazeri S, Mount E, Movassagh R, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Kim EA, Aleiner I, Roushan P. Non-Abelian braiding of graph vertices in a superconducting processor. Nature 2023; 618:264-269. [PMID: 37169834 DOI: 10.1038/s41586-023-05954-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023]
Abstract
Indistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date-including fermions, bosons and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4-8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9-22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23 to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
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Jing J, Ge W, Struck AF, Fernandes MB, Hong S, An S, Fatima S, Herlopian A, Karakis I, Halford JJ, Ng MC, Johnson EL, Appavu BL, Sarkis RA, Osman G, Kaplan PW, Dhakar MB, Jayagopal LA, Sheikh Z, Taraschenko O, Schmitt S, Haider HA, Kim JA, Swisher CB, Gaspard N, Cervenka MC, Rodriguez Ruiz AA, Lee JW, Tabaeizadeh M, Gilmore EJ, Nordstrom K, Yoo JY, Holmes MG, Herman ST, Williams JA, Pathmanathan J, Nascimento FA, Fan Z, Nasiri S, Shafi MM, Cash SS, Hoch DB, Cole AJ, Rosenthal ES, Zafar SF, Sun J, Westover MB. Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs. Neurology 2023; 100:e1737-e1749. [PMID: 36460472 PMCID: PMC10136018 DOI: 10.1212/wnl.0000000000201670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/25/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.
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Affiliation(s)
- Jin Jing
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Wendong Ge
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Aaron F Struck
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Marta Bento Fernandes
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Shenda Hong
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sungtae An
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Safoora Fatima
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Aline Herlopian
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ioannis Karakis
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jonathan J Halford
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Marcus C Ng
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Emily L Johnson
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Brian L Appavu
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Rani A Sarkis
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Gamaleldin Osman
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Peter W Kaplan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Monica B Dhakar
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Lakshman Arcot Jayagopal
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Zubeda Sheikh
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Olga Taraschenko
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sarah Schmitt
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Hiba A Haider
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jennifer A Kim
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Christa B Swisher
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Nicolas Gaspard
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mackenzie C Cervenka
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Andres A Rodriguez Ruiz
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jong Woo Lee
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mohammad Tabaeizadeh
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Emily J Gilmore
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Kristy Nordstrom
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ji Yeoun Yoo
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Manisha G Holmes
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Susan T Herman
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jennifer A Williams
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jay Pathmanathan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Fábio A Nascimento
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ziwei Fan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Samaneh Nasiri
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mouhsin M Shafi
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sydney S Cash
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Daniel B Hoch
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Andrew J Cole
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Eric S Rosenthal
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sahar F Zafar
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jimeng Sun
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - M Brandon Westover
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL.
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Jing J, Ge W, Hong S, Fernandes MB, Lin Z, Yang C, An S, Struck AF, Herlopian A, Karakis I, Halford JJ, Ng MC, Johnson EL, Appavu BL, Sarkis RA, Osman G, Kaplan PW, Dhakar MB, Arcot Jayagopal L, Sheikh Z, Taraschenko O, Schmitt S, Haider HA, Kim JA, Swisher CB, Gaspard N, Cervenka MC, Rodriguez Ruiz AA, Lee JW, Tabaeizadeh M, Gilmore EJ, Nordstrom K, Yoo JY, Holmes MG, Herman ST, Williams JA, Pathmanathan J, Nascimento FA, Fan Z, Nasiri S, Shafi MM, Cash SS, Hoch DB, Cole AJ, Rosenthal ES, Zafar SF, Sun J, Westover MB. Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation. Neurology 2023; 100:e1750-e1762. [PMID: 36878708 PMCID: PMC10136013 DOI: 10.1212/wnl.0000000000207127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/12/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.
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Affiliation(s)
- Jin Jing
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Wendong Ge
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Shenda Hong
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Marta Bento Fernandes
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Zhen Lin
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Chaoqi Yang
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sungtae An
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Aaron F Struck
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Aline Herlopian
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ioannis Karakis
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jonathan J Halford
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Marcus C Ng
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Emily L Johnson
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Brian L Appavu
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Rani A Sarkis
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Gamaleldin Osman
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Peter W Kaplan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Monica B Dhakar
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Lakshman Arcot Jayagopal
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Zubeda Sheikh
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Olga Taraschenko
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sarah Schmitt
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Hiba A Haider
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jennifer A Kim
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Christa B Swisher
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Nicolas Gaspard
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mackenzie C Cervenka
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Andres A Rodriguez Ruiz
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jong Woo Lee
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mohammad Tabaeizadeh
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Emily J Gilmore
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Kristy Nordstrom
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ji Yeoun Yoo
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Manisha G Holmes
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Susan T Herman
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jennifer A Williams
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jay Pathmanathan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Fábio A Nascimento
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ziwei Fan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Samaneh Nasiri
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mouhsin M Shafi
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sydney S Cash
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Daniel B Hoch
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Andrew J Cole
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Eric S Rosenthal
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sahar F Zafar
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jimeng Sun
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - M Brandon Westover
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA.
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21
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Zhuang Y, Shyu CR, Hong S, Li P, Zhang L. Self-sovereign identity empowered non-fungible patient tokenization for health information exchange using blockchain technology. Comput Biol Med 2023; 157:106778. [PMID: 36934533 DOI: 10.1016/j.compbiomed.2023.106778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/30/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND Patient tokenization is a novel approach that allows anonymous patient-level linkage across healthcare facilities, minimizing the risk of breaching protected health information in health information exchange (HIE). Most patient tokenization is the centralized approach that is unable to address data security concerns fundamentally. Non-Fungible Tokens (NFT), which are non-transferable cryptographic assets on the blockchain, have the potential to provide secure, decentralized, and trustworthy patient tokenization. Self-Sovereign Identity (SSI) is a user-centric approach to verify the ownership of NFTs in a decentralized manner. METHODS We have developed a blockchain architecture that contains four modules: (1) Creation module for NFTs creation, (2) Linkage module to link the local patients' accounts to their NFTs, (3) Authentication module that allows patients to permit healthcare providers to access their token, and (4) Exchange module, which involves the HIE process and the validation of the legitimacy of the token through SSI. RESULTS A case study has been conducted on the proposed architecture. Over 3 million transactions have been completed successfully with a blockchain validation and written time of 1.17 s on average. A stability test has also been conducted with a higher throughput of 200 transactions per second running for an hour with an average transaction processing time of 1.42 s. CONCLUSIONS This study proposed a blockchain architecture that achieves SSI-enabled NFT-based patient tokenization. Our architecture design, implementation, and case studies have demonstrated the feasibility and potential of NFT with SSI to establish a secure, transparent, and patient-centric identity management and HIE.
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Affiliation(s)
- Yan Zhuang
- National Institute of Health Data Science, Peking University, Beijing, China; Institute of Medical Technology, Health Science Center of Peking University, Beijing, China
| | - Chi-Ren Shyu
- Institue of Data Science and Informatics, Columbia, MO, USA
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China; Institute of Medical Technology, Health Science Center of Peking University, Beijing, China
| | - Pengfei Li
- National Institute of Health Data Science, Peking University, Beijing, China; Institute of Medical Technology, Health Science Center of Peking University, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, China
| | - Luxia Zhang
- National Institute of Health Data Science, Peking University, Beijing, China; Institute of Medical Technology, Health Science Center of Peking University, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, China.
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22
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Sun C, Li H, Song M, Hong S. A Ranking-Based Cross-Entropy Loss for Early Classification of Time Series. IEEE Trans Neural Netw Learn Syst 2023; PP:1-10. [PMID: 37028352 DOI: 10.1109/tnnls.2023.3250203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Early classification tasks aim to classify time series before observing full data. It is critical in time-sensitive applications such as early sepsis diagnosis in the intensive care unit (ICU). Early diagnosis can provide more opportunities for doctors to rescue lives. However, there are two conflicting goals in the early classification task-accuracy and earliness. Most existing methods try to find a balance between them by weighing one goal against the other. But we argue that a powerful early classifier should always make highly accurate predictions at any moment. The main obstacle is that the key features suitable for classification are not obvious in the early stage, resulting in the excessive overlap of time series distributions in different time stages. The indistinguishable distributions make it difficult for classifiers to recognize. To solve this problem, this article proposes a novel ranking-based cross-entropy () loss to jointly learn the feature of classes and the order of earliness from time series data. In this way, can help classifier to generate probability distributions of time series in different stages with more distinguishable boundary. Thus, the classification accuracy at each time step is finally improved. Besides, for the applicability of the method, we also accelerate the training process by focusing the learning process on high-ranking samples. Experiments on three real-world datasets show that our method can perform classification more accurately than all baselines at all moments.
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23
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Sun C, Li H, Song M, Cai D, Zhang B, Hong S. Adaptive model training strategy for continuous classification of time series. APPL INTELL 2023; 53:1-19. [PMID: 36819946 PMCID: PMC9922045 DOI: 10.1007/s10489-022-04433-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/26/2022] [Indexed: 02/13/2023]
Abstract
The classification of time series is essential in many real-world applications like healthcare. The class of a time series is usually labeled at the final time, but more and more time-sensitive applications require classifying time series continuously. For example, the outcome of a critical patient is only determined at the end, but he should be diagnosed at all times for timely treatment. For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS). Different from the existing single-shot classification, the key of CCTS is to model multiple distributions simultaneously due to the dynamic evolution of time series. But the deep learning model will encounter intertwined problems of catastrophic forgetting and over-fitting when learning multi-distribution. In this work, we found that the well-designed distribution division and replay strategies in the model training process can help to solve the problems. We propose a novel Adaptive model training strategy for CCTS (ACCTS). Its adaptability represents two aspects: (1) Adaptive multi-distribution extraction policy. Instead of the fixed rules and the prior knowledge, ACCTS extracts data distributions adaptive to the time series evolution and the model change; (2) Adaptive importance-based replay policy. Instead of reviewing all old distributions, ACCTS only replays important samples adaptive to their contribution to the model. Experiments on four real-world datasets show that our method outperforms all baselines.
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Affiliation(s)
- Chenxi Sun
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Hongyan Li
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Moxian Song
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Derun Cai
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Baofeng Zhang
- School of Intelligence Science and Technology, Peking University, Beijing, China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Health Science Center of Peking University, Beijing, China
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24
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Akhtar M, Bonus F, Lebrun-Gallagher FR, Johnson NI, Siegele-Brown M, Hong S, Hile SJ, Kulmiya SA, Weidt S, Hensinger WK. A high-fidelity quantum matter-link between ion-trap microchip modules. Nat Commun 2023; 14:531. [PMID: 36754957 PMCID: PMC9908934 DOI: 10.1038/s41467-022-35285-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/25/2022] [Indexed: 02/10/2023] Open
Abstract
System scalability is fundamental for large-scale quantum computers (QCs) and is being pursued over a variety of hardware platforms. For QCs based on trapped ions, architectures such as the quantum charge-coupled device (QCCD) are used to scale the number of qubits on a single device. However, the number of ions that can be hosted on a single quantum computing module is limited by the size of the chip being used. Therefore, a modular approach is of critical importance and requires quantum connections between individual modules. Here, we present the demonstration of a quantum matter-link in which ion qubits are transferred between adjacent QC modules. Ion transport between adjacent modules is realised at a rate of 2424 s-1 and with an infidelity associated with ion loss during transport below 7 × 10-8. Furthermore, we show that the link does not measurably impact the phase coherence of the qubit. The quantum matter-link constitutes a practical mechanism for the interconnection of QCCD devices. Our work will facilitate the implementation of modular QCs capable of fault-tolerant utility-scale quantum computation.
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Affiliation(s)
- M. Akhtar
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - F. Bonus
- Universal Quantum Ltd, Brighton, BN1 6SB UK ,grid.83440.3b0000000121901201Department of Physics and Astronomy, University College London, London, WC1E 6BT UK
| | - F. R. Lebrun-Gallagher
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - N. I. Johnson
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - M. Siegele-Brown
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. Hong
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. J. Hile
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. A. Kulmiya
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,grid.5337.20000 0004 1936 7603Quantum Engineering Centre for Doctoral Training, University of Bristol, Bristol, BS8 1TH UK
| | - S. Weidt
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - W. K. Hensinger
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
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Bang S, Kwon H, Yoon C, Rhew S, Shin D, Moon H, Cho H, Ha U, Lee J, Hong S. Development and validation of a machine learning-based CT radiomics model for differentiation of benign and malignant solid renal tumors. Eur Urol 2023. [DOI: 10.1016/s0302-2838(23)01313-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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26
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Choi H, Pyo KH, Lim S, Cho B, Hong S. PP223 Single-cell RNA sequencing in metastatic lung cancer uncovers the efficacy of PD-1/PD-L1 inhibitors on immune cell population. ESMO Open 2022. [DOI: 10.1016/j.esmoop.2022.100719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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27
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Knight, Imwattana K, Lim SC, Hong S, Putsathit P, Collins DA, Riley TV. WS1.6: GENOMIC EPIDEMIOLOGY OF RECURRENT CLOSTRIDIOIDES DIFFICILE INFECTION IN WESTERN AUSTRALIA. J Glob Antimicrob Resist 2022. [DOI: 10.1016/s2213-7165(22)00274-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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28
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Morvan A, Andersen TI, Mi X, Neill C, Petukhov A, Kechedzhi K, Abanin DA, Michailidis A, Acharya R, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores Burgos L, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Grajales Dau A, Gross JA, Habegger S, Hamilton MC, Harrigan MP, Harrington SD, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Malone F, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Nersisyan A, Newman M, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Olenewa R, Opremcak A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shvarts V, Skruzny J, Smith WC, Strain D, Sterling G, Su Y, Szalay M, Torres A, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Xing C, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Aleiner I, Ioffe LB, Roushan P. Formation of robust bound states of interacting microwave photons. Nature 2022; 612:240-245. [PMID: 36477133 PMCID: PMC9729104 DOI: 10.1038/s41586-022-05348-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/14/2022] [Indexed: 12/12/2022]
Abstract
Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2-9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.
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Affiliation(s)
- A Morvan
- Google Research, Mountain View, CA, USA
| | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J Basso
- Google Research, Mountain View, CA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | - D Eppens
- Google Research, Mountain View, CA, USA
| | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Computation and Communication Technology, Centre for Quantum Software and Information, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Y Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - F Malone
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | - K C Miao
- Google Research, Mountain View, CA, USA
| | - M Mohseni
- Google Research, Mountain View, CA, USA
| | | | - E Mount
- Google Research, Mountain View, CA, USA
| | | | - O Naaman
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - R Olenewa
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | | | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
| | - Z Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
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- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - I Aleiner
- Google Research, Mountain View, CA, USA.
| | - L B Ioffe
- Google Research, Mountain View, CA, USA.
| | - P Roushan
- Google Research, Mountain View, CA, USA.
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Hahn T, Daymont C, Beukelman T, Groh B, Hays K, Bingham CA, Scalzi L, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Intraarticular steroids as DMARD-sparing agents for juvenile idiopathic arthritis flares: Analysis of the Childhood Arthritis and Rheumatology Research Alliance Registry. Pediatr Rheumatol Online J 2022; 20:107. [PMID: 36434731 PMCID: PMC9701017 DOI: 10.1186/s12969-022-00770-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/08/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Children with juvenile idiopathic arthritis (JIA) who achieve a drug free remission often experience a flare of their disease requiring either intraarticular steroids (IAS) or systemic treatment with disease modifying anti-rheumatic drugs (DMARDs). IAS offer an opportunity to recapture disease control and avoid exposure to side effects from systemic immunosuppression. We examined a cohort of patients treated with IAS after drug free remission and report the probability of restarting systemic treatment within 12 months. METHODS We analyzed a cohort of patients from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry who received IAS for a flare after a period of drug free remission. Historical factors and clinical characteristics and of the patients including data obtained at the time of treatment were analyzed. RESULTS We identified 46 patients who met the inclusion criteria. Of those with follow up data available 49% had restarted systemic treatment 6 months after IAS injection and 70% had restarted systemic treatment at 12 months. The proportion of patients with prior use of a biologic DMARD was the only factor that differed between patients who restarted systemic treatment those who did not, both at 6 months (79% vs 35%, p < 0.01) and 12 months (81% vs 33%, p < 0.05). CONCLUSION While IAS are an option for all patients who flare after drug free remission, it may not prevent the need to restart systemic treatment. Prior use of a biologic DMARD may predict lack of success for IAS. Those who previously received methotrexate only, on the other hand, are excellent candidates for IAS.
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Affiliation(s)
- Timothy Hahn
- Department of Pediatrics, Penn State Children's Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA, 17033-0855, USA.
| | - Carrie Daymont
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Timothy Beukelman
- grid.265892.20000000106344187Department of Pediatrics, University of Alabama at Birmingham, CPPN G10, 1600 7th Ave South, Birmingham, AL 35233 USA
| | - Brandt Groh
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | | | - Catherine April Bingham
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Lisabeth Scalzi
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
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He LN, Fu S, Ma H, Chen C, Zhang X, Li H, Du W, Chen T, Jiang Y, Wang Y, Wang Y, Zhou Y, Lin Z, Yang Y, Huang Y, Zhao H, Fang W, Zhang H, Zhang L, Hong S. Early on-treatment tumor growth rate (EOT-TGR) determines treatment outcomes of advanced non-small-cell lung cancer patients treated with programmed cell death protein 1 axis inhibitor. ESMO Open 2022; 7:100630. [PMID: 36442353 PMCID: PMC9808481 DOI: 10.1016/j.esmoop.2022.100630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/02/2022] [Accepted: 10/09/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Tumor growth rate (TGR), denoted as percentage change in tumor size per month, is a well-established indicator of tumor growth kinetics. The predictive value of early on-treatment TGR (EOT-TGR) for immunotherapy remains unclear. We sought to establish and validate the association of EOT-TGR with treatment outcomes in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 (programmed cell death protein 1/programmed death-ligand 1) therapy. PATIENTS AND METHODS This bicenter retrospective cohort study included a training cohort, a contemporaneously treated internal validation cohort, and an external validation cohort. Computed tomography images were retrieved to calculate EOT-TGR, denoted as tumor burden change per month during a period between baseline and the first imaging evaluation after immunotherapy. Kaplan-Meier methodology and Cox regression analysis were conducted for survival analyses. RESULTS In the pooled cohort (n = 172), 125 patients (72.7%) were males; median age at diagnosis was 58 (range 28-79) years. Based on the training cohort, we determined the optimal cut-off value for EOT-TGR as 10.4%/month. Higher EOT-TGR was significantly associated with inferior overall survival [OS; hazard ratio (HR) 2.93, 95% confidence interval (CI) 1.47-5.83; P = 0.002], worse progression-free survival (PFS; HR 2.44, 95% CI 1.46-4.08; P = 0.001), and lower objective response rate (3.3% versus 20.9%; P = 0.040) and durable clinical benefit rate (6.7% versus 41.9%; P = 0.001). Results were reproducible in the two validation cohorts for OS and PFS. Among 43 patients who had a best response of progressive disease in the training cohort, those with high EOT-TGR had worse OS (HR 2.64; P = 0.041) and were more likely to progress due to target lesions at the first tumor evaluation (85.2% versus 0.0%; P <0.001). CONCLUSIONS Higher EOT-TGR was associated with inferior OS and immunotherapeutic response in patients with aNSCLC undergoing anti-PD-1/PD-L1 therapy. This easy-to-calculate radiologic biomarker may help evaluate the abilities of immunotherapy to prolong survival and assist in tailoring patients' management. TRIAL REGISTRATION ClinicalTrials.govNCT04722406; https://clinicaltrials.gov/ct2/show/NCT04722406.
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Affiliation(s)
- L.-N. He
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - S. Fu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation of Sun Yat-Sen University; Department of Cellular & Molecular Diagnostics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - H. Ma
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - C. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Departments of Radiation Oncology, Guangzhou, China
| | - X. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Li
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Du
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - T. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Endoscopy, Guangzhou, China
| | - Y. Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,VIP Region, Guangzhou, China
| | - Z. Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhang
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China,Prof. Haibo Zhang, Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, 111 Dade Road, Guangzhou, Guangdong 510120, People’s Republic of China. Tel: +86-20-81887233-34830
| | - L. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Prof. Li Zhang, MD, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87343458
| | - S. Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Correspondence to: Prof. Shaodong Hong, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87342480
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Mi X, Sonner M, Niu MY, Lee KW, Foxen B, Acharya R, Aleiner I, Andersen TI, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Brill L, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Debroy DM, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores L, Forati E, Fowler AG, Giang W, Gidney C, Gilboa D, Giustina M, Dau AG, Gross JA, Habegger S, Harrigan MP, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Kafri D, Kechedzhi K, Khattar T, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Lee J, Laws L, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Morvan A, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Newman M, O’Brien TE, Opremcak A, Petukhov A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schuster C, Shearn MJ, Shvarts V, Strain D, Su Y, Szalay M, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Abanin DA, Roushan P. Noise-resilient edge modes on a chain of superconducting qubits. Science 2022; 378:785-790. [DOI: 10.1126/science.abq5769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the one-dimensional kicked Ising model, which exhibits nonlocal Majorana edge modes (MEMs) with
ℤ
2
parity symmetry. We find that any multiqubit Pauli operator overlapping with the MEMs exhibits a uniform late-time decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This characteristic allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Furthermore, the MEMs are found to be resilient against certain symmetry-breaking noise owing to a prethermalization mechanism. Our work elucidates the complex interplay between noise and symmetry-protected edge modes in a solid-state environment.
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Affiliation(s)
- X. Mi
- Google Research, Mountain View, CA, USA
| | - M. Sonner
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - M. Y. Niu
- Google Research, Mountain View, CA, USA
| | - K. W. Lee
- Google Research, Mountain View, CA, USA
| | - B. Foxen
- Google Research, Mountain View, CA, USA
| | | | | | | | - F. Arute
- Google Research, Mountain View, CA, USA
| | - K. Arya
- Google Research, Mountain View, CA, USA
| | - A. Asfaw
- Google Research, Mountain View, CA, USA
| | | | - J. C. Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J. Basso
- Google Research, Mountain View, CA, USA
| | | | | | | | - L. Brill
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Chen
- Google Research, Mountain View, CA, USA
| | - B. Chiaro
- Google Research, Mountain View, CA, USA
| | | | - P. Conner
- Google Research, Mountain View, CA, USA
| | | | | | | | - S. Demura
- Google Research, Mountain View, CA, USA
| | | | - D. Eppens
- Google Research, Mountain View, CA, USA
| | | | - L. Faoro
- Google Research, Mountain View, CA, USA
| | - E. Farhi
- Google Research, Mountain View, CA, USA
| | - R. Fatemi
- Google Research, Mountain View, CA, USA
| | - L. Flores
- Google Research, Mountain View, CA, USA
| | - E. Forati
- Google Research, Mountain View, CA, USA
| | | | - W. Giang
- Google Research, Mountain View, CA, USA
| | - C. Gidney
- Google Research, Mountain View, CA, USA
| | - D. Gilboa
- Google Research, Mountain View, CA, USA
| | | | - A. G. Dau
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - S. Hong
- Google Research, Mountain View, CA, USA
| | - T. Huang
- Google Research, Mountain View, CA, USA
| | - A. Huff
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Jiang
- Google Research, Mountain View, CA, USA
| | - C. Jones
- Google Research, Mountain View, CA, USA
| | - D. Kafri
- Google Research, Mountain View, CA, USA
| | | | | | - S. Kim
- Google Research, Mountain View, CA, USA
| | - A. Y. Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | | | - A. N. Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P. Laptev
- Google Research, Mountain View, CA, USA
| | - K.-M. Lau
- Google Research, Mountain View, CA, USA
| | - J. Lee
- Google Research, Mountain View, CA, USA
| | - L. Laws
- Google Research, Mountain View, CA, USA
| | - W. Liu
- Google Research, Mountain View, CA, USA
| | | | - O. Martin
- Google Research, Mountain View, CA, USA
| | | | - M. McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | | | | | | | - A. Morvan
- Google Research, Mountain View, CA, USA
| | - E. Mount
- Google Research, Mountain View, CA, USA
| | | | - O. Naaman
- Google Research, Mountain View, CA, USA
| | - M. Neeley
- Google Research, Mountain View, CA, USA
| | - C. Neill
- Google Research, Mountain View, CA, USA
| | - M. Newman
- Google Research, Mountain View, CA, USA
| | | | | | | | - R. Potter
- Google Research, Mountain View, CA, USA
| | | | | | - N. Saei
- Google Research, Mountain View, CA, USA
| | - D. Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - D. Strain
- Google Research, Mountain View, CA, USA
| | - Y. Su
- Google Research, Mountain View, CA, USA
| | - M. Szalay
- Google Research, Mountain View, CA, USA
| | - G. Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T. White
- Google Research, Mountain View, CA, USA
| | - Z. Yao
- Google Research, Mountain View, CA, USA
| | - P. Yeh
- Google Research, Mountain View, CA, USA
| | - J. Yoo
- Google Research, Mountain View, CA, USA
| | | | - Y. Zhang
- Google Research, Mountain View, CA, USA
| | - N. Zhu
- Google Research, Mountain View, CA, USA
| | - H. Neven
- Google Research, Mountain View, CA, USA
| | - D. Bacon
- Google Research, Mountain View, CA, USA
| | - J. Hilton
- Google Research, Mountain View, CA, USA
| | - E. Lucero
- Google Research, Mountain View, CA, USA
| | | | - S. Boixo
- Google Research, Mountain View, CA, USA
| | | | - Y. Chen
- Google Research, Mountain View, CA, USA
| | - J. Kelly
- Google Research, Mountain View, CA, USA
| | | | - D. A. Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
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Wei G, Di X, Zhang W, Geng S, Zhang D, Wang K, Fu Z, Hong S. Estimating critical values from electrocardiogram using a deep ordinal convolutional neural network. BMC Med Inform Decis Mak 2022; 22:295. [PMID: 36384646 PMCID: PMC9670442 DOI: 10.1186/s12911-022-02035-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 10/28/2022] [Indexed: 11/17/2022] Open
Abstract
Background Critical values are commonly used in clinical laboratory tests to define health-related conditions of varying degrees. Knowing the values, people can quickly become aware of health risks, and the health professionals can take immediate actions and save lives. Methods In this paper, we propose a method that extends the concept of critical value to one of the most commonly used physiological signals in the clinical environment—Electrocardiogram (ECG). We first construct a mapping from common ECG diagnostic conclusions to critical values. After that, we build a 61-layer deep convolutional neural network named CardioV, which is characterized by an ordinal classifier. Results We conduct experiments on a large public ECG dataset, and demonstrate that CardioV achieves a mean absolute error of 0.4984 and a ROC-AUC score of 0.8735. In addition, we find that the model performs better for extreme critical values and the younger age group, while gender does not affect the performance. The ablation study confirms that the ordinal classification mechanism suits for estimating the critical values which contain ranking information. Moreover, model interpretation techniques help us discover that CardioV focuses on the characteristic ECG locations during the critical value estimation process. Conclusions As an ordinal classifier, CardioV performs well in estimating ECG critical values that can help people quickly identify different heart conditions. We obtain ROC-AUC scores above 0.8 for all four critical value categories, and find that the extreme values (0 (no risk) and 3 (high risk)) have better model performance than the other two (1 (low risk) and 2 (medium risk)). Results also show that gender does not affect the performance, and the older age group has worse performance than the younger age group. In addition, visualization techniques reveal that the model pays more attention to characteristic ECG locations.
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Hong S, Lee J, Heo J, Suh K, Kim S, Kim Y, Kim J, Lee JS. 413P Association of concomitant medications on survival outcomes in cancer patients treated with immune checkpoint inhibitors: Analysis of health insurance review and assessment database. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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Hall J, Sud S, Casey D, Poellmann M, Bu J, Wang A, Hong S, Shen C. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Locoregional Head and Neck Cancer Receiving Definitive Therapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Song S, Kim J, Nam J, Ko Y, Kim J, Jung S, Kang S, Park J, Seo H, Kim H, Jeong B, Kim T, Choi S, Nam J, Ku J, Joo K, Jang W, Yoon Y, Yun S, Hong S, Oh J. Stage matched head-to-head comparison between urachal carcinoma and urothelial bladder cancer: TNM-stage based analysis from a national multicenter database. EUR UROL SUPPL 2022. [DOI: 10.1016/s2666-1683(22)02591-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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Yu M, Yun M, Lee S, Rajasekaran N, Park K, Kim N, Hong S, Oh S, Lee Y, Lee E, Kim C, Lim S, Choi J, Cho B. 1174P The MET inhibitor ABN401 in combination with the third-generation EGFR-TKI is effective MET-amplified and EGFR-mutant NSCLC with acquired resistance to third-generation EGFR-TKI in preclinical models. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.1297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Yan Y, Hong S, Zhang W, Li H. Artificial Intelligence in Skin Diseases: Fulfilling its Potentials to Meet the Real Needs in Dermatology Practice. Health Data Sci 2022; 2022:9791467. [PMID: 38487488 PMCID: PMC10880148 DOI: 10.34133/2022/9791467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 03/17/2024]
Affiliation(s)
- Yicen Yan
- Department of Dermatology and Venereology, Peking University First Hospital, No. 8 Xishiku St., Xicheng District, Beijing 100034, China
- National Clinical Research Center for Skin and Immune Diseases, No. 8 Xishiku St., Xicheng District, Beijing 100034China
- Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, No. 8 Xishiku St., Xicheng District, Beijing 100034China
- NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, No. 8 Xishiku St., Xicheng District, Beijing 100034China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, No. 38 Xueyuan Rd, Haidian District, Beijing 100191, China
| | - Wensheng Zhang
- Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing 100190, China
| | - Hang Li
- Department of Dermatology and Venereology, Peking University First Hospital, No. 8 Xishiku St., Xicheng District, Beijing 100034, China
- National Clinical Research Center for Skin and Immune Diseases, No. 8 Xishiku St., Xicheng District, Beijing 100034China
- Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, No. 8 Xishiku St., Xicheng District, Beijing 100034China
- NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, No. 8 Xishiku St., Xicheng District, Beijing 100034China
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Feng Y, Geng S, Chu J, Fu Z, Hong S. Building and training a deep spiking neural network for ECG classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Ramesh P, Jaishankar D, Cosgrove C, Kosche C, Li A, Hong S, Shivde R, Munir S, Zhang H, Choi J, Le Poole I. 318 Skin rash composition after checkpoint inhibitor therapy varies by therapeutic regimen. J Invest Dermatol 2022. [DOI: 10.1016/j.jid.2022.05.326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Sun C, Hong S, Wang J, Dong X, Han F, Li H. A systematic review of deep learning methods for modeling electrocardiograms during sleep. Physiol Meas 2022; 43. [PMID: 35853448 DOI: 10.1088/1361-6579/ac826e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 07/19/2022] [Indexed: 11/11/2022]
Abstract
Sleep is one of the most important human physiological activities and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone to errors. Current research has confirmed the correlations between sleep and the respiratory/circulatory system. Electrocardiography (ECG) is convenient to perform, and ECG data are rich in breathing information. Therefore, sleep research based on ECG data has become popular. Currently, deep learning (DL) methods have achieved promising results on predictive health care tasks using ECG signals. Therefore, in this review, we systematically identify recent research studies and analyze them from the perspectives of data, model, and task. We discuss the shortcomings, summarize the findings, and highlight the potential opportunities. For sleep-related tasks, many ECG-based DL methods produce more accurate results than traditional approaches by combining multiple signal features and model structures. Methods that are more interpretable, scalable, and transferable will become ubiquitous in the daily practice of medicine and ambient-assisted-living applications. This paper is the first systematic review of ECG-based DL methods for sleep tasks.
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Affiliation(s)
- Chenxi Sun
- School of Artificial Intelligence, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, 100871, CHINA
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, Beijing, 100871, CHINA
| | - Jingyu Wang
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Xiaosong Dong
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Fang Han
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Hongyan Li
- School of Artificial Intelligence, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, Beijing, 100871, CHINA
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Kang E, Kim YG, Oh JS, Hong S, Lee CK, Yoo B, Ahn SM. POS1247 THE EFFECT OF IMMUNOSUPPRESSIVE AGENTS ON ANTIBODY FORMATION AFTER COVID-19 VACCINATION IN RHEUMATOID ARTHRITIS PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundThere is still controversy about the efficacy of COVID-19 vaccination and its extent in lowering immunogenicity of Rheumatoid Arthritis (RA) patients. The guideline in whether immunosuppressive agents need to be discontinued before the vaccination is continuously updated because it is considered to lower immunogenicity. Furthermore, there is great discussion on the effectiveness of the COVID-19 booster vaccine and interest in antibody generation in different types of vaccine, as in South Korea there are many patients who were prescribed the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine.ObjectivesThus, we investigated the differences of antibody production between patients who received only two doses of ChAdOx1-S nCoV-19 and those who received the mRNA booster vaccine. Also, antibody production under different types of immunosuppressive agents was analyzed.MethodsFrom October 14, 2021 to January 21, 2022 at a tertiary referral center, two patient groups diagnosed with RA were studied prospectively; one group that completed 1st and 2nd doses of ChAdOx1-S nCoV-19 vaccine, second group that completed mRNA booster vaccine as well as two doses of ChAdOx1-S nCoV-19 vaccine. SARS-CoV-2 antibody testing on the semiquantitative anti-SARS-CoV-2 S enzyme immunoassay was done, and differences in antibody titers were analyzed in patients who received different immunosuppressive agents such as csDMARD, TNF inhibitor, JAK inhibitor, Tocilizumab, Abatacept and Corticosteroid. Statistical analysis with a multivariate logistic regression model was performed.ResultsIn a total of 261 patients, 153 patients had completed two doses of ChAdOx1-S nCoV-19, 108 patients had completed third mRNA booster vaccine. Anti-SARS-CoV-2 RBD antibody positive rate (titer>0.8U/mL) was 97%(149/153) and 99%(107/108) respectively, and only 5 patients showed negative result. In the aspect of high antibody titer(>250U/mL), which is the upper limit of the RBD antibody immunoassay, the result showed rate of 31% (47/153) in the non-booster group and 94%(102/108) in the booster group respectively.Among the different immunosuppressive agents and other clinical aspects, multivariate analysis revealed that corticosteroid use (OR 0.91; 95% CI: 0.86-0.98), older age(OR 4.33; 95% CI: 1.34-13.91), and male gender(OR 0.35; 95% CI 0.16-0.75) were significantly associated with low rate of high antibody titer.Furthermore, out of 14 patients who underwent antibody test twice before and after the mRNA booster vaccine, other than four patients who already showed high titer of >250U/mL before the mRNA booster vaccine, 10 patients showed an increase in titer after the booster vaccine and 7 patients were acquired high titer of >250U/mL.Figure 1.Anti-SARS-CoV RBD antibody titer of two groupsTable 1.Analysis of immunosuppressive agents and other clinical aspects for high antibody titer(>250U/mL) after two doses of ChAdOx1-S nCoV-19Univariate analysisMultivariate analysisParameterOR95% CIp valueOR95% CIp valueClinical features Age0.9170.860-0.9780.0080.9170.857-0.9810.012 Sex3.6741.206-11.1910.0224.3301.348-13.9120.014 DAS 281.1440.670-1.9500.622 Duration0.9300.830-1.0430.214Medications csDMARD1.2730.639-2.5331.273 TNF inhibitor2.2110.795-6.1450.128 JAK inhibitor0.6650.275-1.6070.365 Abatacept0.3680.038-3.6020.391 Tocilizumab1.2640.438-3.6480.665 Corticosteroid0.4720.235-0.9490.0350.3490.163-0.7480.007Medication dose Methotrexate0.9930.919-1.0720.855 Corticosteroid0.8490.719-1.0030.054ConclusionAnti-SARS-CoV-2 RBD antibody positive rate was 97% or more regardless of the mRNA booster vaccination. However, patients who received the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine showed high antibody titer (>250U/mL) three times more than those who did not receive the booster shot.Our findings also showed that corticosteroid use, old age, and male gender is significantly associated with low rate of acquiring high antibody titer.Disclosure of InterestsNone declared
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Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0476 PREDICTIVE FACTORS FOR THE DEVELOPMENT OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH IMMUNE THROMBOCYTOPENIA. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.1436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundPatients with immune thrombocytopenia (ITP) have a risk of developing systemic lupus erythematosus (SLE). We sought to examine the clinical characteristics of patients with primary ITP who later developed SLE, and identified the risk factors for the development of SLE.ObjectivesWe retrospectively examined patients who were diagnosed with primary ITP at a tertiary hospital between August 2001 and November 2019. We compared the clinical characteristics according to the development of SLE. Logistic regression analysis was performed to identify the factors associated with the development of SLE.MethodsOf 130 patients with primary ITP, 10 (7.7%) were later diagnosed with SLE during follow-up (median, 30 months [IQR, 15.5–105]). The presence of skin bleeding, organ bleeding, lymphopenia, anemia, and positive antinuclear antibody (ANA) titer (> 1:160) were more common among patients who later developed SLE than did those who did not develop SLE. Multivariate analysis showed that young age (< 40 years; odds ratio [OR], 8.359 [95% confidence interval (CI), 1.230–56.793]; p = 0.033), organ bleeding (OR, 18.349 [95% CI, 2.771–121.517]; p = 0.003), and ANA positivity (>1:160; OR, 7.692 [95% CI, 1.482–39.910]; p = 0.015) were significantly associated with the development of SLE.ResultsYoung age (< 40 years), organ bleeding, and ANA positivity (> 1:160) were risk factors for the development of SLE in patients with primary ITP.ConclusionThese results suggest that continued follow-up for the detection of SLE development is needed for patients with ITP, particularly those with young age, ANA positivity, or organ bleeding.References[1]Zhu, Fang-Xiao, et al. “Risk of systemic lupus erythematosus in patients with idiopathic thrombocytopenic purpura: a population-based cohort study.” Annals of the rheumatic diseases 79.6 (2020): 793-799.Table 1.Factors associated with the development of SLE in patients with primary ITPUnivariateMultivariateOR95% CIP valueOR95% CIP valueYoung agea5.4441.332–22.2500.0188.3591.230–56.7930.033Female4.3330.530–35.4220.17BMI0.8730.717–1.0700.20Skin bleeding8.4191.034–68.5330.046Mucosa bleeding1.2500.247–6.3300.79Organ bleeding14.8643.633–60.815< 0.00118.3492.771–121.5170.003Platelet counts0.9110.828–1.0020.06ANA positivityb16.5003.984–68.341< 0.0017.6921.482–39.9100.015Neutropeniac2.1110.229–19.4990.51Lymphopeniad4.8461.189–19.7590.028Anemiae10.1182.044–50.0910.005SLE: systemic lupus erythematosus, ITP: immune thrombocytopenia, BMI: body mass index, ANA: antinuclear antibody, OR: odds ratio, CI: confidence interval.aYoung age = age < 40 yearsbANA positivity ≥ 1:160cNeutropenia = Absolute neutrophil count < 1500 μLdLymphopenia = Absolute lymphocyte count < 1500 μLeAnemia = Hemoglobin < 12 g/dLDisclosure of InterestsNone declared
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Kim YE, Choi SJ, Lim DH, Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0456 DISEASE FLARE OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH END-STAGE RENAL DISEASE ON DIALYSIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BackgroundThe systemic lupus erythematosus (SLE) disease activity in patients with lupus nephritis (LN) generally declines after the initiation of renal replacement therapy (RRT); this is known as the “burn out” phenomenon that possibly occurs due to the suppression of cellular and humoral immunity in the end-stage renal disease (ESRD) state and elimination of disease pathogenic factor by dialysis [1-4]. However, several studies showed that SLE flares could occur even during RRT [5-8]. Nevertheless, the details of disease flares of SLE in patients under dialysis have not been studied yet.ObjectivesThis study aimed to investigate the clinical features, risk factors, and treatment details of SLE patients experiencing disease flare under RRT.MethodsThe medical records of SLE patients who received dialysis at two tertiary referral hospitals in Seoul and Ulsan, South Korea were reviewed. All patients in this study were either clinically or histologically diagnosed with LNResultsOf a total of 121 patients with SLE on dialysis, 96 (79.3%) were on hemodialysis (HD) and 25 (20.6%) were on peritoneal dialysis (PD). During a median follow-up of 45 months (IQR, 23–120) after the initiation of dialysis, 32 (26.4%) patients experienced SLE flare (HD, n = 25; PD, n = 7). The most common features of SLE flare were hematologic (40.6%) and constitutional manifestations (40.6%). Treatments for disease flares were based on corticosteroids, and 11 (34.3%) patients required additional immunosuppressants including cyclophosphamide and mycophenolate mofetil. There was no case of severe adverse events related to medication. non-renal SLE Disease Activity Index (SLEDAI) score before dialysis initiation (HR 1.235; 95% CI, 1.122–1.359; P = 0.001) was a significant risk factor for disease flare during dialysis.Table 1.Multivariable analysis of factors associated with SLE flare under dialysisHazard ratio95% CIP-valueNon-renal SLEDAI at the initiation of dialysis1.2351.122–1.3590.001Hematologic manifestation prior to dialysis1.2560.690–2.8260.150Cumulative amount of steroid during 1 year prior to the initiation of dialysis1.0400.995–1.0870.086Dialysis modality: hemodialysis0.7660.262–2.2430.630ConclusionMore than one-quarter of SLE patients experienced disease flare during dialysis, which most commonly had hematologic manifestations. Continued follow-up and appropriate treatments including immunosuppressants should be considered for patients with SLE under dialysis.References[1]Coplon NS, Diskin CJ, Petersen J, Swenson RS. The Long-Term Clinical Course of Systemic Lupus Erythematosus in End-Stage Renal Disease. New England Journal of Medicine 1983;308:186-90.[2]Lee P-T, Fang H-C, Chen C-L, Chiou Y-H, Chou K-J, Chung H-M. Poor prognosis of end-stage renal disease in systemic lupus erythematosus: a cohort of Chinese patients. Lupus 2003;12:827-32.[3]Pahl MV, Gollapudi S, Sepassi L, Gollapudi P, Elahimehr R, Vaziri ND. Effect of end-stage renal disease on B-lymphocyte subpopulations, IL-7, BAFF and BAFF receptor expression. Nephrology Dialysis Transplantation 2010;25:205-12.[4]Ribeiro FM, Fabris CL, Bendet I, Lugon JR. Survival of lupus patients on dialysis: a Brazilian cohort. Rheumatology 2013;52:494-500.[5]Okano K, Yumura W, Nitta K et al. Analysis of Lupus Activity in End-Stage Renal Disease Treated by Hemodialysis. Internal Medicine 2001;40:598-602.[6]Barrera-Vargas A, Quintanar-Martínez M, Merayo-Chalico J, Alcocer-Varela J, Gómez-Martín D. Risk factors for systemic lupus erythematosus flares in patients with end-stage renal disease: a case–control study. Rheumatology 2015:kev349.[7]Cucchiari D, Graziani G, Ponticelli C. The dialysis scenario in patients with systemic lupus erythematosus. Nephrology Dialysis Transplantation 2014;29:1507-13.[8]Kang S-H, Chung B-H, Choi S-R et al. Comparison of Clinical Outcomes by Different Renal Replacement Therapy in Patients with End-Stage Renal Disease Secondary to Lupus Nephritis. The Korean Journal of Internal Medicine 2011;26:60.Disclosure of InterestsNone declared
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Nam SH, Ahn SM, Oh JS, Hong S, Lee CK, Yoo B, Kim YG. AB1273 MACROPHAGE ACTIVATION SYNDROME IN RHEUMATIC DISEASE: CLINICAL CHARACTERISTICS AND PROGNOSIS OF 20 PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundMacrophage activation syndrome (MAS) is a hyperinflammatory condition that is known to be secondary hemophagocytic lymphohistiocytosis (HLH) in patients with rheumatic disease.ObjectivesThe aim of study was to evaluate the clinical manifestations and outcomes in patients with MAS with rheumatic disease.MethodsWe performed a retrospective study of 20 adult patients who were diagnosed with MAS from 2012 to 2020. MAS was classified according to the HLH-2004 criteria. Patients’ information, including clinical features, laboratory findings, and treatment regimens, was collected, and the overall survival rate was estimated by the Kaplan–Meier method.ResultsTwenty patients (18 women, 35.6 ± 18.3 years) who met the HLH-2004 criteria also fulfilled the 2016 EULAR/ACR/PRINTO classification criteria for MAS, and HScore was higher than 169 (median, 238.5). Fourteen patients with systemic lupus erythematosus and 6 patients with adult-onset Still’s disease were included. All patients were treated initially with corticosteroids, and 16 patients required additional immunosuppressants. The overall survival at 3 and 6 months was 75.2% and 64.3%. In survivors, renal impairment was less common (23.1% versus 42.9%, p = 0.007), the levels of AST (202.0 versus 72.0 IU/L, p = 0.006) and LDH (1144.0 versus 343.0IU/L, p = 0.001), and platelet count (90.0 versus 46.0 × 109/L, p = 0.016) were higher in compared to non-survivors. Nine patients had opportunistic infections, five of whom died during admission.ConclusionThe mortality of patients with MAS remains high. Renal impairment, levels of AST and LDH, and platelet count might be associated with prognosis.Table 1.Treatments and management characteristics of patients with MASNo.Age/sexDiseaseDisease duration (months)1st Treatment (corticosteroids)2nd Treatment3rd TreatmentCombined infectionAlive/dead119/FSLE11 mg/kgIVIG + PPTCZ, RTXBacteremiaDead220/MSLE01 mg/kg---Alive320/FAOSD11 mg/kgVP16--Alive422/FSLE1100 mgIVIG + PP-PneumoniaDead522/FAOSD0500 mgIVIG--Alive623/FSLE1821 mg/kg---Alive723/FSLE411 mg/kg---Alive830/FSLE1461 mg/kgIVIGCsA-Alive932/FSLE1271 mg/kgIVIG + PPCsA, TCZPneumoniaAlive1035/FAOSD01 mg/kgCsA-Viral infectionAlive1137/FSLE651 mg/kgCsA, VP16-BacteremiaAlive1238/FSLE01 mg/kgIVIG + PPRTX-Dead1340/FAOSD00.5 mg/kgCsA--Alive1443/FSLE601 mg/kgIVIG + PPTCZ, RTX, CsA,PCP,DeadVP16, IFXViral infection1549/FSLE01 mg/kgCYC-BacteremiaAlive1651/FAOSD01 mg/kg---Alive1757/FSLE01 mg/kgIVIG + PPCsA, VP16Fungal infectionDead1861/FSLE21 mg/kgIVIG + PPTCZ-Dead1968/FSLE21 mg/kgIVIG + PPCsAFungal infectionAlive2070/MAOSD01 mg/kgIVIG + PPCsA, VP16Fungal infectionDeadSLE: Systemic lupus erythematosus, IVIG: Intravenous immunoglobulin, PP: Plasmapheresis, TCZ: Tocilizumab, RTX: Rituximab, AOSD: Adult-onset still’s disease, VP16: Etoposide, PCP: Pneumocystis pneumonia, CsA: Cyclosporin, IFX: Infliximab, MCTD: Mixed connective tissue disease.Disclosure of InterestsNone declared
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Harid NM, Jing J, Hogan J, Nascimento FA, Ouyang A, Zheng WL, Ge W, Zafar SF, Kim JA, Lam AD, Herlopian A, Maus D, Karakis I, Ng M, Hong S, Zhu Y, Kaplan PW, Cash S, Shafi M, Martz G, Halford JJ, Westover MB. Measuring expertise in identifying interictal epileptiform discharges. Epileptic Disord 2022; 24:496-506. [PMID: 35770748 PMCID: PMC9340812 DOI: 10.1684/epd.2021.1409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 09/08/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.
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Affiliation(s)
- Nitish M. Harid
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Jacob Hogan
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | | | - An Ouyang
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Wei-Long Zheng
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Jennifer A. Kim
- Department of Neurology, Yale School of Medicine, New Haven CT, USA
| | - Alice D. Lam
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Aline Herlopian
- Department of Neurology, Yale School of Medicine, New Haven CT, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta GA, USA
| | - Marcus Ng
- Department of Neurology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing China
| | - Yu Zhu
- Xuanwu Hospital, Capital Medical University, Beijing China
| | - Peter W. Kaplan
- Department of Neurology, Johns Hopkins University School of Medicine, Bayview Medical Center, Baltimore, MD, USA
| | - Sydney Cash
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Mouhsin Shafi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gabriel Martz
- Department of Neurology, Hartford HealthCare Medical Group at Hartford Hospital, CT, USA
| | - Jonathan J. Halford
- Department of Neurology, Medical University of South Carolina, Charleston SC, USA
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Kang E, Hong S, Kim YG, Lee CK, Oh JS, Yoo B, Ahn SM. POS0762 LONG-TERM RENAL OUTCOMES OF PATIENTS WITH NON-PROLIFERATIVE LUPUS NEPHRITIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundAlthough proliferative (class III or IV) lupus nephritis (LN) is the most common finding in the classification of LN, pure membranous (class V) or mesangial (class I or II) LN can occur as a form of LN. Even though non-proliferative LN (class I, II, or V) is a less severe form with good outcomes, data on long-term renal prognosis are limited.ObjectivesThis study investigated the long-term outcomes and prognostic factors in non-proliferative LN.MethodsWe retrospectively reviewed the medical records of patients with systemic lupus erythematosus who were diagnosed with LN class I, II, V or II+IV by kidney biopsy between 1997 and 2021 at a tertiary referral center. Clinical and laboratory data were compared between patients with and without poor renal outcomes. Poor renal outcome was defined as an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 or death due to renal cause. Univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the factors associated with poor renal outcomes.ResultsWe included 71 patients with non-proliferative LN (4: class I; 17: class II; 48: class V, 17; 2: class II+V). Median follow-up duration was 103 months (interquartile range 27–185) and the overall rate of poor renal outcomes at last follow-up was 29% (21/71), including end-stage renal disease (n=2) and renal death (n=1).Univariate analysis indicated that older age (HR 1.05; 95% CI: 1.00–1.09), low eGFR (HR 0.97; 95% CI: 0.95–0.99) and failure to reach complete remission at 6 months (HR 0.332; 95% CI: 0.12–0.92) were significantly associated with poor renal outcomes. Multivariate analysis revealed that low eGFR at 6 months (HR 0.97; 95% CI: 0.95–0.99) was significantly associated with poor renal outcomes.Figure 1.Renal outcomes at last follow upeGFR, estimated glomerular filtration rate (ml/min/1.73m2)Table 1.Univariate and multivariate Cox proportional hazard regression analyses of the factor associated with poor renal outcomesParameterUnivariate analysisMultivariate analysisHR95% CIp valueHR95% CIp valueClinical features Age1.0461.003-1.0910.0361.0020.960-1.0470.921 Sex1.6540.375-7.2980.506 SLEDAI1.0360.965-1.1120.327 Extra renal SLEDAI1.0380.971-1.110.272Renal profiles eGFR at LN diagnosis0.9930.976-1.0110.456 Proteinuria at LN diagnosis1.0001.000-1.0000.444 > 1g/24 hours0.6690.243-1.8410.437 > 3g/24 hours0.6240.229-1.6990.356 eGFR at 6M0.9670.948-0.9860.0010.9680.948-0.9880.002 eGFR at 12M0.9640.947-0.9810.000 Complete remission at 6M0.3320.119-0.9240.0350.5530.179-1.7070.303 Complete remission at 12M0.6670.232-1.9140.451 Transformation1.2460.423-0.7010.692Laboratory data Anti-dsDNA1.0010.999-1.0030.196 C31.0201.000-1.0410.051 C41.0270.969-1.0890.367 Albumin1.1800.661-2.1090.576ClassificationaClass I0.8020.102-6.3030.834Class II1.2980.412-4.0880.656Class V0.8870.308-2.5570.824Class II+V0.0480.000-16850.837Medicationsb ACEi/ARB1.6520.603-4.5280.329 Hydroxychloroquine1.3260.414-4.2420.635 Corticosteroid1.1860.154-9.1080.870 CNI2.4390.464-12.8240.292 MMF3.7880.959-14.9650.057 AZA0.5890.133-2.6110.486a LN classifications were based on the International Society of Pathology/Renal Pathology Society (ISN/RPS) classification.b Medications maintained at least one year since Lupus Nephritis diagnosis.HR, hazard ratio; 95% CI, 95% confidence interval; SLEDAI, systemic lupus erythematosus disease activity index; eGFR, estimated glomerular filtration rate; LN, lupus nephritis; anti-dsDNA, anti-double strand DNA; C3/C4; complement 3/4; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CNI, carcineurin inhibitor; MMF, mycophenolate mofetil; AZA, azathioprine.ConclusionPoor renal outcomes occurred in approximately 30% of patients with non-proliferative LN (class I, II or V) after long-term follow-up.Our findings suggest that more active management may be needed for non-proliferative LN, particularly in patients with low eGFR at 6 months.Disclosure of InterestsNone declared
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Zhou Y, Chen Y, Zhang D, Geng S, Wei G, Tian Y, Hong S, LIU XINGPENG. PO-661-03 USE OF A DEEP LEARNING ALGORITHM TO PREDICT PAROXYSMAL ATRIAL FIBRILLATION BASED ON PRINTED ELECTROCARDIOGRAPHIC RECORDS ACQUIRED DURING SINUS RHYTHM. Heart Rhythm 2022. [DOI: 10.1016/j.hrthm.2022.03.327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Chidambaram S, Hong S, Simpson M, Osazuwa-Peters N, Ward G, Massa S. Temporal Trends in Oropharyngeal Cancer Incidence, Survival, and Cancer-Directed Surgery Among Elderly Americans. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2021.12.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Fu Z, Wang C, Wei G, Zhang W, Du S, Hong S. HITS: Binarizing physiological time series with deep hashing neural network. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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