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Kiser AC, Shi J, Bucher BT. An explainable long short-term memory network for surgical site infection identification. Surgery 2024; 176:24-31. [PMID: 38616153 PMCID: PMC11162927 DOI: 10.1016/j.surg.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/23/2024] [Accepted: 03/03/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Currently, surgical site infection surveillance relies on labor-intensive manual chart review. Recently suggested solutions involve machine learning to identify surgical site infections directly from the medical record. Deep learning is a form of machine learning that has historically performed better than traditional methods while being harder to interpret. We propose a deep learning model, a long short-term memory network, for the identification of surgical site infection from the medical record with an attention layer for explainability. METHODS We retrieved structured data and clinical notes from the University of Utah Health System's electronic health care record for operative events randomly selected for manual chart review from January 2016 to June 2021. Surgical site infection occurring within 30 days of surgery was determined according to the National Surgical Quality Improvement Program definition. We trained the long short-term memory model along with traditional machine learning models for comparison. We calculated several performance metrics from a holdout test set and performed additional analyses to understand the performance of the long short-term memory, including an explainability analysis. RESULTS Surgical site infection was present in 4.7% of the total 9,185 operative events. The area under the receiver operating characteristic curve and sensitivity of the long short-term memory was higher (area under the receiver operating characteristic curve: 0.954, sensitivity: 0.920) compared to the top traditional model (area under the receiver operating characteristic curve: 0.937, sensitivity: 0.736). The top 5 features of the long short-term memory included 2 procedure codes and 3 laboratory values. CONCLUSION Surgical site infection surveillance is vital for the reduction of surgical site infection rates. Our explainable long short-term memory achieved a comparable area under the receiver operating characteristic curve and greater sensitivity when compared to traditional machine learning methods. With explainable deep learning, automated surgical site infection surveillance could replace burdensome manual chart review processes.
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Affiliation(s)
- Amber C Kiser
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT.
| | - Jianlin Shi
- Division of Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT
| | - Brian T Bucher
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT; Division of Pediatric Surgery, Department of Surgery, University of Utah School of Medicine, Salt Lake City, UT
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2
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Zhang Q, Coury R, Tang W. Prediction of conversion from mild cognitive impairment to Alzheimer's disease and simultaneous feature selection and grouping using Medicaid claim data. Alzheimers Res Ther 2024; 16:54. [PMID: 38461266 PMCID: PMC10924319 DOI: 10.1186/s13195-024-01421-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/27/2024] [Indexed: 03/11/2024]
Abstract
BACKGROUND Due to the heterogeneity among patients with Mild Cognitive Impairment (MCI), it is critical to predict their risk of converting to Alzheimer's disease (AD) early using routinely collected real-world data such as the electronic health record data or administrative claim data. METHODS The study used MarketScan Multi-State Medicaid data to construct a cohort of MCI patients. Logistic regression with tree-guided lasso regularization (TGL) was proposed to select important features and predict the risk of converting to AD. A subsampling-based technique was used to extract robust groups of predictive features. Predictive models including logistic regression, generalized random forest, and artificial neural network were trained using the extracted features. RESULTS The proposed TGL workflow selected feature groups that were robust, highly interpretable, and consistent with existing literature. The predictive models using TGL selected features demonstrated higher prediction accuracy than the models using all features or features selected using other methods. CONCLUSIONS The identified feature groups provide insights into the progression from MCI to AD and can potentially improve risk prediction in clinical practice and trial recruitment.
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Affiliation(s)
- Qi Zhang
- Department of Mathematics and Statistics, University of New Hampshire, Durham, NH, 03824, USA.
| | - Ron Coury
- Department of Mathematics and Statistics, University of New Hampshire, Durham, NH, 03824, USA
| | - Wenlong Tang
- Takeda Pharmaceuticals, Cambridge, MA, 02142, USA
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3
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Tang AS, Rankin KP, Cerono G, Miramontes S, Mills H, Roger J, Zeng B, Nelson C, Soman K, Woldemariam S, Li Y, Lee A, Bove R, Glymour M, Aghaeepour N, Oskotsky TT, Miller Z, Allen IE, Sanders SJ, Baranzini S, Sirota M. Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights. NATURE AGING 2024; 4:379-395. [PMID: 38383858 PMCID: PMC10950787 DOI: 10.1038/s43587-024-00573-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024]
Abstract
Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.
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Affiliation(s)
- Alice S Tang
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, San Francisco and Berkeley, CA, USA.
| | - Katherine P Rankin
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Gabriel Cerono
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Silvia Miramontes
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Hunter Mills
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Jacquelyn Roger
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Billy Zeng
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Charlotte Nelson
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Karthik Soman
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah Woldemariam
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Yaqiao Li
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Albert Lee
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Riley Bove
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Maria Glymour
- Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Zachary Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Isabel E Allen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Stephan J Sanders
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Sergio Baranzini
- Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Department of Pediatrics, University of California, San Francisco, CA, USA.
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4
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Maritsch M, Föll S, Lehmann V, Styger N, Bérubé C, Kraus M, Feuerriegel S, Kowatsch T, Züger T, Fleisch E, Wortmann F, Stettler C. Smartwatches for non-invasive hypoglycaemia detection during cognitive and psychomotor stress. Diabetes Obes Metab 2024; 26:1133-1136. [PMID: 38086545 DOI: 10.1111/dom.15402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 02/06/2024]
Affiliation(s)
- Martin Maritsch
- Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
| | - Simon Föll
- Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
| | - Vera Lehmann
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern, University Hospital, University of Bern, Bern, Switzerland
| | - Naïma Styger
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern, University Hospital, University of Bern, Bern, Switzerland
| | - Caterina Bérubé
- Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
| | - Mathias Kraus
- School of Business, Economics and Society, Friedrich-Alexander University Erlangen-Nürnberg, Nürnberg, Germany
| | - Stefan Feuerriegel
- Institute of AI in Management, LMU Munich, Munich, Germany
- Munich Center for Machine Learning (MCML), Munich, Germany
| | - Tobias Kowatsch
- Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St Gallen, St Gallen, Switzerland
| | - Thomas Züger
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern, University Hospital, University of Bern, Bern, Switzerland
- Department of Endocrinology and Metabolic Diseases, Kantonsspital Olten, Olten, Switzerland
| | - Elgar Fleisch
- Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
- Institute of Technology Management, University of St Gallen, St Gallen, Switzerland
| | - Felix Wortmann
- Institute of Technology Management, University of St Gallen, St Gallen, Switzerland
| | - Christoph Stettler
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern, University Hospital, University of Bern, Bern, Switzerland
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5
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Chen K, Weng Y, Hosseini AA, Dening T, Zuo G, Zhang Y. A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer's Disease involving data synthesis. Neural Netw 2024; 169:442-452. [PMID: 37939533 DOI: 10.1016/j.neunet.2023.10.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/23/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disease that commonly occurs in older people. It is characterized by both cognitive and functional impairment. However, as AD has an unclear pathological cause, it can be hard to diagnose with confidence. This is even more so in the early stage of Mild Cognitive Impairment (MCI). This paper proposes a U-Net based Generative Adversarial Network (GAN) to synthesize fluorodeoxyglucose - positron emission tomography (FDG-PET) from magnetic resonance imaging - T1 weighted imaging (MRI-T1WI) for further usage in AD diagnosis including its early-stage MCI. The experiments have displayed promising results with Structural Similarity Index Measure (SSIM) reaching 0.9714. Furthermore, three types of classifiers are developed, i.e., one Multi-Layer Perceptron (MLP) based classifier, two Graph Neural Network (GNN) based classifiers where one is for graph classification and the other is for node classification. 10-fold cross-validation has been conducted on all trials of experiments for classifier comparison. The performance of these three types of classifiers has been compared with the different input modalities setting and data fusion strategies. The results have shown that GNN based node classifier surpasses the other two types of classifiers, and has achieved the state-of-the-art (SOTA) performance with the best accuracy at 90.18% for 3-class classification, namely AD, MCI and normal control (NC) with the synthesized fluorodeoxyglucose - positron emission tomography (FDG-PET) features fused at the input level. Moreover, involving synthesized FDG-PET as part of the input with proper data fusion strategies has also proved to enhance all three types of classifiers' performance. This work provides support for the notion that machine learning-derived image analysis may be a useful approach to improving the diagnosis of AD.
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Affiliation(s)
- Ke Chen
- School of Computer Science, University of Nottingham Ningbo China, Ningbo, 315100, China; Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China; School of Medicine, University of Nottingham, Nottingham, NG7 2RD, UK.
| | - Ying Weng
- School of Computer Science, University of Nottingham Ningbo China, Ningbo, 315100, China.
| | - Akram A Hosseini
- Neurology Department, Nottingham University Hospitals NHS Trust, Nottingham, NG7 2UH, UK.
| | - Tom Dening
- School of Medicine, University of Nottingham, Nottingham, NG7 2RD, UK.
| | - Guokun Zuo
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
| | - Yiming Zhang
- School of Computer Science, University of Nottingham Ningbo China, Ningbo, 315100, China; School of Medicine, University of Nottingham, Nottingham, NG7 2RD, UK.
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6
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Bednorz A, Mak JKL, Jylhävä J, Religa D. Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future. Clin Interv Aging 2023; 18:2171-2183. [PMID: 38152074 PMCID: PMC10752027 DOI: 10.2147/cia.s400887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/05/2023] [Indexed: 12/29/2023] Open
Abstract
Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and disease risk prediction. EMRs also offer a range of advantages in clinical practice, such as comprehensive medical records, streamlined communication with healthcare providers, remote data access, and rapid retrieval of test results, ultimately leading to increased efficiency, enhanced patient safety, and improved quality of care in gerontology, which includes benefits like reduced medication use and better patient history taking and physical examination assessments. The use of artificial intelligence (AI) and machine learning (ML) approaches on EMRs can further improve disease diagnosis, symptom classification, and support clinical decision-making. However, there are also challenges related to data quality, data entry errors, as well as the ethics and safety of using AI in healthcare. This article discusses the future of EMRs in gerontology and the application of AI and ML in clinical research. Ethical and legal issues surrounding data sharing and the need for healthcare professionals to critically evaluate and integrate these technologies are also emphasized. The article concludes by discussing the challenges related to the use of EMRs in research as well as in their primary intended use, the daily clinical practice.
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Affiliation(s)
- Adam Bednorz
- John Paul II Geriatric Hospital, Katowice, Poland
- Institute of Psychology, Humanitas Academy, Sosnowiec, Poland
| | - Jonathan K L Mak
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland
| | - Dorota Religa
- Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden
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7
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Fouladvand S, Noshad M, Periyakoil VJ, Chen JH. Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease. Health Sci Rep 2023; 6:e1438. [PMID: 37867782 PMCID: PMC10584995 DOI: 10.1002/hsr2.1438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 05/22/2023] [Accepted: 07/03/2023] [Indexed: 10/24/2023] Open
Affiliation(s)
- Sajjad Fouladvand
- Stanford Center for Biomedical Informatics ResearchStanford UniversityStanfordCaliforniaUSA
| | - Morteza Noshad
- Stanford Center for Biomedical Informatics ResearchStanford UniversityStanfordCaliforniaUSA
| | | | - Jonathan H. Chen
- Stanford Center for Biomedical Informatics ResearchStanford UniversityStanfordCaliforniaUSA
- Division of Hospital MedicineStanford UniversityStanfordCaliforniaUSA
- Clinical Excellence Research CenterStanford UniversityStanfordCaliforniaUSA
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8
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Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87:103705. [PMID: 37506575 DOI: 10.1016/j.ajp.2023.103705] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
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Affiliation(s)
- Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Beijing 100191, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Qun-Xi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - San-Wang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yong-Bo Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Xiao-Xing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Tang-Sheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Kai Yuan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Ying Han
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China.
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Soloveva MV, Poudel G, Barnett A, Shaw JE, Martino E, Knibbs LD, Anstey KJ, Cerin E. Characteristics of urban neighbourhood environments and cognitive age in mid-age and older adults. Health Place 2023; 83:103077. [PMID: 37451077 DOI: 10.1016/j.healthplace.2023.103077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/29/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023]
Abstract
In this cross-sectional study, we examined the extent to which features of the neighbourhood natural, built, and socio-economic environments were related to cognitive age in adults (N = 3418, Mage = 61 years) in Australia. Machine learning estimated an individual's cognitive age from assessments of processing speed, verbal memory, premorbid intelligence. A 'cognitive age gap' was calculated by subtracting chronological age from predicted cognitive age and was used as a marker of cognitive age. Greater parkland availability and higher neighbourhood socio-economic status were associated with a lower cognitive age gap score in confounder- and mediator-adjusted regression models. Cross-sectional design is a limitation. Living in affluent neighbourhoods with access to parks maybe beneficial for cognitive health, although selection mechanisms may contribute to the findings.
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Affiliation(s)
- Maria V Soloveva
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, 3000, Australia.
| | - Govinda Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, 3000, Australia
| | - Anthony Barnett
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, 3000, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia; School of Life Sciences, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Erika Martino
- School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3053, Australia
| | - Luke D Knibbs
- School of Public Health, The University of Sydney, NSW 2006, Australia; Public Health Research Analytics and Methods for Evidence, Public Health Unit, Sydney Local Health District, Camperdown, NSW 2050, Australia
| | - Kaarin J Anstey
- School of Psychology, University of New South Wales, Kensington, NSW, 2052, Australia; Neuroscience Research Australia (NeuRA), Sydney, NSW, 2031, Australia; UNSW Ageing Futures Institute, Kensington, NSW, 2052, Australia
| | - Ester Cerin
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, 3000, Australia; School of Public Health, The University of Hong Kong, Hong Kong, China; Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia; Department of Community Medicine, UiT the Artic University of Norway, 9019, Tromsø, Norway
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10
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Mazzeo S, Lassi M, Padiglioni S, Vergani AA, Moschini V, Scarpino M, Giacomucci G, Burali R, Morinelli C, Fabbiani C, Galdo G, Amato LG, Bagnoli S, Emiliani F, Ingannato A, Nacmias B, Sorbi S, Grippo A, Mazzoni A, Bessi V. PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol. BMC Neurol 2023; 23:300. [PMID: 37573339 PMCID: PMC10422810 DOI: 10.1186/s12883-023-03347-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia. METHODS We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD. DISCUSSION This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD. TRIAL REGISTRATION NUMBER (TRN) NCT05569083.
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Affiliation(s)
- Salvatore Mazzeo
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Michael Lassi
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Sonia Padiglioni
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
- Regional Referral Centre for Relational Criticalities - Tuscany Region, Florence, Italy
| | - Alberto Arturo Vergani
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Valentina Moschini
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | | | - Giulia Giacomucci
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | | | - Carmen Morinelli
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | | | - Giulia Galdo
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | - Lorenzo Gaetano Amato
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Silvia Bagnoli
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | - Filippo Emiliani
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | - Assunta Ingannato
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | | | - Alberto Mazzoni
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Valentina Bessi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy.
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
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van der Haar D, Moustafa A, Warren SL, Alashwal H, van Zyl T. An Alzheimer's disease category progression sub-grouping analysis using manifold learning on ADNI. Sci Rep 2023; 13:10483. [PMID: 37380746 DOI: 10.1038/s41598-023-37569-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 06/23/2023] [Indexed: 06/30/2023] Open
Abstract
Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further. Specifically, we used Spectral embedding, Multidimensional scaling, Isomap, t-Distributed Stochastic Neighbour Embedding, Uniform Manifold Approximation and Projection, and sparse denoising autoencoder based manifolds on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We then determine the clustering potential of the learned embeddings and then determine if category sub-groupings or sub-categories can be found. We then used a Kruskal-sWallis H test to determine the statistical significance of the discovered AD subcategories. Our results show that the existing AD categories do exhibit sub-groupings, especially in mild cognitive impairment transitions in many of the tested manifolds, showing there may be a need for further subcategories to describe AD progression.
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Affiliation(s)
- Dustin van der Haar
- Academy of Computer Science and Software Engineering, University of Johannesburg, Gauteng, South Africa.
| | - Ahmed Moustafa
- Department of Human Anatomy and Physiology, University of Johannesburg, Gauteng, South Africa
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD, Australia
| | - Samuel L Warren
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD, Australia
| | - Hany Alashwal
- College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates
| | - Terence van Zyl
- Institute for Intelligent Systems, University of Johannesburg, Gauteng, South Africa
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12
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Birri Makota RB, Musenge E. Predicting HIV infection in the decade (2005-2015) pre-COVID-19 in Zimbabwe: A supervised classification-based machine learning approach. PLOS DIGITAL HEALTH 2023; 2:e0000260. [PMID: 37285368 DOI: 10.1371/journal.pdig.0000260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 04/24/2023] [Indexed: 06/09/2023]
Abstract
The burden of HIV and related diseases have been areas of great concern pre and post the emergence of COVID-19 in Zimbabwe. Machine learning models have been used to predict the risk of diseases, including HIV accurately. Therefore, this paper aimed to determine common risk factors of HIV positivity in Zimbabwe between the decade 2005 to 2015. The data were from three two staged population five-yearly surveys conducted between 2005 and 2015. The outcome variable was HIV status. The prediction model was fit by adopting 80% of the data for learning/training and 20% for testing/prediction. Resampling was done using the stratified 5-fold cross-validation procedure repeatedly. Feature selection was done using Lasso regression, and the best combination of selected features was determined using Sequential Forward Floating Selection. We compared six algorithms in both sexes based on the F1 score, which is the harmonic mean of precision and recall. The overall HIV prevalence for the combined dataset was 22.5% and 15.3% for females and males, respectively. The best-performing algorithm to identify individuals with a higher likelihood of HIV infection was XGBoost, with a high F1 score of 91.4% for males and 90.1% for females based on the combined surveys. The results from the prediction model identified six common features associated with HIV, with total number of lifetime sexual partners and cohabitation duration being the most influential variables for females and males, respectively. In addition to other risk reduction techniques, machine learning may aid in identifying those who might require Pre-exposure prophylaxis, particularly women who experience intimate partner violence. Furthermore, compared to traditional statistical approaches, machine learning uncovered patterns in predicting HIV infection with comparatively reduced uncertainty and, therefore, crucial for effective decision-making.
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Affiliation(s)
- Rutendo Beauty Birri Makota
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Eustasius Musenge
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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13
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Qasrawi R, Vicuna Polo S, Abu Khader R, Abu Al-Halawa D, Hallaq S, Abu Halaweh N, Abdeen Z. Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments. Front Psychiatry 2023; 14:1071622. [PMID: 37304448 PMCID: PMC10250653 DOI: 10.3389/fpsyt.2023.1071622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Mental health and cognitive development are critical aspects of a child's overall well-being; they can be particularly challenging for children living in politically violent environments. Children in conflict areas face a range of stressors, including exposure to violence, insecurity, and displacement, which can have a profound impact on their mental health and cognitive development. Methods This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. Results This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. Discussion The findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine
- Department of Computer Engineering, Istinye University, Istanbul, Türkiye
| | - Stephanny Vicuna Polo
- Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine
| | - Rami Abu Khader
- Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine
| | | | - Sameh Hallaq
- Al-Quds Bard College for Arts and Sciences, Al-Quds University, Jerusalem, Palestine
| | - Nael Abu Halaweh
- Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine
| | - Ziad Abdeen
- Faculty of Medicine, Al-Quds University, Jerusalem, Palestine
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14
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Zhong S, Ma F, Gao J, Bian L. Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20105865. [PMID: 37239591 DOI: 10.3390/ijerph20105865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/27/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023]
Abstract
Urban dwellers are exposed to communicable diseases, such as influenza, in various urban spaces. Current disease models are able to predict health outcomes at the individual scale but are mostly validated at coarse scales due to the lack of fine-scaled ground truth data. Further, a large number of transmission-driving factors have been considered in these models. Because of the lack of individual-scaled validations, the effectiveness of factors at their intended scale is not substantiated. These gaps significantly undermine the efficacy of the models in assessing the vulnerability of individuals, communities, and urban society. The objectives of this study are twofold. First, we aim to model and, most importantly, validate influenza-like illness (ILI) symptoms at the individual scale based on four sets of transmission-driving factors pertinent to home-work space, service space, ambient environment, and demographics. The effort is supported by an ensemble approach. For the second objective, we investigate the effectiveness of the factor sets through an impact analysis. The validation accuracy reaches 73.2-95.1%. The validation substantiates the effectiveness of factors pertinent to urban spaces and unveils the underlying mechanism that connects urban spaces and population health. With more fine-scaled health data becoming available, the findings of this study may see increasing value in informing policies that improve population health and urban livability.
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Affiliation(s)
- Shiran Zhong
- Department of Geography, University of Western Ontario, London, ON N6A 3K7, Canada
| | - Fenglong Ma
- College of Information Sciences and Technology, Pennsylvania State University, University Park, State College, PA 16802, USA
| | - Jing Gao
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Ling Bian
- Department of Geography, University at Buffalo, The State University of New York, Buffalo, NY 14261, USA
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15
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Lee YC, Jung SH, Kumar A, Shim I, Song M, Kim MS, Kim K, Myung W, Park WY, Won HH. ICD2Vec: Mathematical representation of diseases. J Biomed Inform 2023; 141:104361. [PMID: 37054960 DOI: 10.1016/j.jbi.2023.104361] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 03/31/2023] [Accepted: 04/05/2023] [Indexed: 04/15/2023]
Abstract
BACKGROUND The International Classification of Diseases (ICD) codes represent the global standard for reporting disease conditions. The current ICD codes connote direct human-defined relationships among diseases in a hierarchical tree structure. Representing the ICD codes as mathematical vectors helps to capture nonlinear relationships in medical ontologies across diseases. METHODS We propose a universally applicable framework called "ICD2Vec" designed to provide mathematical representations of diseases by encoding corresponding information. First, we present the arithmetical and semantic relationships between diseases by mapping composite vectors for symptoms or diseases to the most similar ICD codes. Second, we investigated the validity of ICD2Vec by comparing the biological relationships and cosine similarities among the vectorized ICD codes. Third, we propose a new risk score called IRIS, derived from ICD2Vec, and demonstrate its clinical utility with large cohorts from the UK and South Korea. RESULTS Semantic compositionality was qualitatively confirmed between descriptions of symptoms and ICD2Vec. For example, the most diseases most similar to COVID-19 were found to be the common cold (ICD-10: J00), unspecified viral hemorrhagic fever (ICD-10: A99), and smallpox (ICD-10: B03). We show the significant associations between the cosine similarities derived from ICD2Vec and the biological relationships using disease-to-disease pairs. Furthermore, we observed significant adjusted hazard ratios (HR) and area under the receiver operating characteristics (AUROC) between IRIS and risks for eight diseases. For instance, the higher IRIS for coronary artery disease (CAD) can be the higher probability for the incidence of CAD (HR: 2.15 [95% CI 2.02-2.28] and AUROC: 0.587 [95% CI 0.583-0.591]). We identified individuals at substantially increased risk of CAD using IRIS and 10-year atherosclerotic cardiovascular disease risk (adjusted HR, 4.26, 95% CI, 3.59-5.05). CONCLUSIONS ICD2Vec, a proposed universal framework for converting qualitatively measured ICD codes into quantitative vectors containing semantic relationships between diseases, exhibited a significant correlation with actual biological significance. In addition, the IRIS was a significant predictor of major diseases in a prospective study using two large-scale Biobank EHR datasets. Based on this clinical validity and utility evidence, we suggest that publicly available ICD2Vec can be used in diverse research and clinical practices and has important clinical implications.
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Affiliation(s)
- Yeong Chan Lee
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Aman Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Minku Song
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Min Seo Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea; Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea; Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.
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Shastry KA, Sattar SA. Logistic random forest boosting technique for Alzheimer’s diagnosis. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY 2023; 15:1719-1731. [PMID: 37056794 PMCID: PMC9983513 DOI: 10.1007/s41870-023-01187-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 02/16/2023] [Indexed: 03/06/2023]
Abstract
Alzheimer's disease (AD) is a common and well-known neurodegenerative condition that causes cognitive impairment. In the field of medicine, it is the "nervous system" disorder that has received the most attention. Despite this extensive research, there is no treatment or strategy to slow or stop its spread. Nevertheless, there are a variety of options (medication and non-medication alternatives) that may aid in the treatment of AD symptoms at their various phases, thereby enhancing the patient's quality of life. As AD advances over time, it is necessary to treat patients at their various stages appropriately. As a result, detecting and classifying AD phases prior to symptom treatment can be beneficial. Approximately twenty years ago, the rate of progress in the field of machine learning (ML) accelerated dramatically. Using ML methods, this study focuses on early AD identification. The "Alzheimer's Disease Neuroimaging Initiative" (ADNI) dataset was subjected to exhaustive testing for AD identification. The purpose was to classify the dataset into three groups: AD, "Cognitive Normal" (CN), and "Late Mild Cognitive Impairment" (LMCI). In this paper, we present the ensemble model Logistic Random Forest Boosting (LRFB), representing the ensemble of “Logistic Regression” (LR), “Random Forest” (RF), and “Gradient Boost” (GB). The proposed LRFB outperformed LR, RF, GB, “k-Nearest Neighbour” (k-NN), “Multi-Layer Perceptron” (MLP), “Support Vector Machine” (SVM), “AdaBoost” (AB), “Naïve Bayes” (NB), “XGBoost” (XGB), “Decision Tree” (DT), and other ensemble ML models with respect to the performance metrics “Accuracy” (Acc), “Recall” (Rec), “Precision” (Prec), and “F1-Score” (FS).
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17
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Skolariki K, Exarchos TP, Vlamos P. Computational Models for Biomarker Discovery. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:289-295. [PMID: 37486506 DOI: 10.1007/978-3-031-31982-2_33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder characterized by progressive cognitive decline. Early diagnosis and accurate prediction of disease progression are critical for developing effective therapeutic interventions. In recent years, computational models have emerged as powerful tools for biomarker discovery and disease prediction in Alzheimer's and other neurodegenerative diseases. This paper explores the use of computational models, particularly machine learning techniques, in analyzing large volumes of data and identifying patterns related to disease progression. The significance of early diagnosis, the challenges in classifying patients at the mild cognitive impairment (MCI) stage, and the potential of computational models to improve diagnostic accuracy are examined. Furthermore, the importance of incorporating diverse biomarkers, including genetic, molecular, and neuroimaging indicators, to enhance the predictive capabilities of these models is highlighted. The paper also presents case studies on the application of computational models in simulating disease progression, analyzing neurodegenerative cascades, and predicting the future development of Alzheimer's. Overall, computational models for biomarker discovery offer promising opportunities to advance our understanding of Alzheimer's disease, facilitate early diagnosis, and guide the development of targeted therapeutic strategies.
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18
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John LH, Kors JA, Fridgeirsson EA, Reps JM, Rijnbeek PR. External validation of existing dementia prediction models on observational health data. BMC Med Res Methodol 2022; 22:311. [PMID: 36471238 PMCID: PMC9720950 DOI: 10.1186/s12874-022-01793-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/15/2022] [Indexed: 12/07/2022] Open
Abstract
BACKGROUND Many dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records. METHODS We identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters' Dementia Risk Score, Mehta's RxDx-Dementia Risk Index, and Nori's ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models. RESULTS We reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67-0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69-0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62-0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard. CONCLUSION We observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.
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Affiliation(s)
- Luis H. John
- grid.5645.2000000040459992XDepartment of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Jan A. Kors
- grid.5645.2000000040459992XDepartment of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Egill A. Fridgeirsson
- grid.5645.2000000040459992XDepartment of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Jenna M. Reps
- grid.497530.c0000 0004 0389 4927Janssen Research and Development, 1125 Trenton Harbourton Rd, NJ 08560 Titusville, USA
| | - Peter R. Rijnbeek
- grid.5645.2000000040459992XDepartment of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
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19
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Dai X, Park JH, Yoo S, D'Imperio N, McMahon BH, Rentsch CT, Tate JP, Justice AC. Survival analysis of localized prostate cancer with deep learning. Sci Rep 2022; 12:17821. [PMID: 36280773 PMCID: PMC9592586 DOI: 10.1038/s41598-022-22118-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 10/10/2022] [Indexed: 01/20/2023] Open
Abstract
In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics [Formula: see text] over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved [Formula: see text] 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained [Formula: see text] 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.
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Affiliation(s)
- Xin Dai
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA.
| | - Ji Hwan Park
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
- School of Computer Science, The University of Oklahoma, Norman, OK, USA
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
| | - Nicholas D'Imperio
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
| | - Benjamin H McMahon
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Christopher T Rentsch
- VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Janet P Tate
- VA Connecticut Healthcare System, West Haven, CT, USA
- Schools of Medicine and Public Health, Yale University, New Haven, CT, USA
| | - Amy C Justice
- VA Connecticut Healthcare System, West Haven, CT, USA
- Schools of Medicine and Public Health, Yale University, New Haven, CT, USA
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20
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Poudel GR, Barnett A, Akram M, Martino E, Knibbs LD, Anstey KJ, Shaw JE, Cerin E. Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10977. [PMID: 36078704 PMCID: PMC9517821 DOI: 10.3390/ijerph191710977] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/26/2022] [Accepted: 08/31/2022] [Indexed: 06/02/2023]
Abstract
The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34-97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data.
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Affiliation(s)
- Govinda R. Poudel
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
| | - Anthony Barnett
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
| | - Muhammad Akram
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
| | - Erika Martino
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Luke D. Knibbs
- School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia
- Public Health Unit, Sydney Local Health District, Camperdown, NSW 2050, Australia
| | - Kaarin J. Anstey
- School of Psychology, University of New South Wales, Sydney, NSW 2052, Australia
- UNSW Ageing Futures Institute, University of New South Wales, Sydney, NSW 2052, Australia
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
| | - Jonathan E. Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Ester Cerin
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
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21
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Chen YH, Chen Q, Kong L, Liu G. Early detection of autism spectrum disorder in young children with machine learning using medical claims data. BMJ Health Care Inform 2022. [PMCID: PMC9462117 DOI: 10.1136/bmjhci-2022-100544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Objectives Early diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict the risk of ASD in young children between 18 months and 30 months based on their medical histories using real-world health claims data. Methods Using the MarketScan Health Claims Database 2005–2016, we identified 12 743 children with ASD and a random sample of 25 833 children without ASD as our study cohort. We developed logistic regression (LR) with least absolute shrinkage and selection operator and random forest (RF) models for predicting ASD diagnosis at ages of 18–30 months, using demographics, medical diagnoses and healthcare service procedures extracted from individual’s medical claims during early years postbirth as predictor variables. Results For predicting ASD diagnosis at age of 24 months, the LR and RF models achieved the area under the receiver operating characteristic curve (AUROC) of 0.758 and 0.775, respectively. Prediction accuracy further increased with age. With predictor variables separated by outpatient and inpatient visits, the RF model for prediction at age of 24 months achieved an AUROC of 0.834, with 96.4% specificity and 20.5% positive predictive value at 40% sensitivity, representing a promising improvement over the existing screening tool in practice. Conclusions Our study demonstrates the feasibility of using machine learning models and health claims data to identify children with ASD at a very young age. It is deemed a promising approach for monitoring ASD risk in the general children population and early detection of high-risk children for targeted screening.
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Affiliation(s)
- Yu-Hsin Chen
- The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Qiushi Chen
- The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Lan Kong
- Department of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Guodong Liu
- Department of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Department of Psychiatry and Behavioral Health, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Department of Pediatrics, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- The Center for Applied Studies in Health Economics (CASHE), The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
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22
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Augmented risk of dementia in hypertrophic cardiomyopathy: A propensity score matching analysis using the nationwide cohort. PLoS One 2022; 17:e0269911. [PMID: 35709174 PMCID: PMC9202937 DOI: 10.1371/journal.pone.0269911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 05/31/2022] [Indexed: 11/19/2022] Open
Abstract
Background Dementia is a big medical and socioeconomic problem on aging society, and cardiac diseases have already shown a significant contribution to developing dementia. However, the risk of dementia related to hypertrophic cardiomyopathy (HCM), the most common inherited cardiomyopathy, has never been evaluated. Methods In a large-scale longitudinal cohort using National Health Insurance database, 4,645 subjects with HCM aged ≥50 years between 2010 and 2016 were collected and matched with 13,935 controls, based on propensity scores (1:3). We investigated the incidence and risk of dementia, Alzheimer’s disease (AD), and vascular dementia (VaD) between groups. Results During follow-up (median 3.9 years after 1-year lag), incident dementia occurred in 739 subjects (4.0%): 78.2% for AD and 13.0% for VaD. The incidence of dementia, AD, and VaD were 23.0, 18.0, and 2.9/1,000 person-years, respectively, and was generally more prevalent in HCM. HCM group had a 50% increased risk of dementia, particularly AD, whereas there was no difference in the risk of VaD. The impact of HCM on AD (HR 1.52, 95% CI 1.26–1.84, p<0.001) was comparable with that of diabetes mellitus and smoking. Increased risk of AD in relation to HCM was consistent in various subgroups including younger healthier population. Conclusions This is the first to demonstrate the increased risk of dementia, mainly AD rather than VaD, in subjects with HCM. Early surveillance and active prevention for cognitive impairment could help for a better quality of life in an era that HCM is considered a chronic manageable disease with low mortality.
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23
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Dementia classification using MR imaging and clinical data with voting based machine learning models. MULTIMEDIA TOOLS AND APPLICATIONS 2022. [DOI: 10.1007/s11042-022-12754-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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24
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Xu X, Ge Z, Chow EPF, Yu Z, Lee D, Wu J, Ong JJ, Fairley CK, Zhang L. A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months. J Clin Med 2022; 11:jcm11071818. [PMID: 35407428 PMCID: PMC8999359 DOI: 10.3390/jcm11071818] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/18/2022] [Accepted: 03/23/2022] [Indexed: 11/16/2022] Open
Abstract
Background: More than one million people acquire sexually transmitted infections (STIs) every day globally. It is possible that predicting an individual’s future risk of HIV/STIs could contribute to behaviour change or improve testing. We developed a series of machine learning models and a subsequent risk-prediction tool for predicting the risk of HIV/STIs over the next 12 months. Methods: Our data included individuals who were re-tested at the clinic for HIV (65,043 consultations), syphilis (56,889 consultations), gonorrhoea (60,598 consultations), and chlamydia (63,529 consultations) after initial consultations at the largest public sexual health centre in Melbourne from 2 March 2015 to 31 December 2019. We used the receiver operating characteristic (AUC) curve to evaluate the model’s performance. The HIV/STI risk-prediction tool was delivered via a web application. Results: Our risk-prediction tool had an acceptable performance on the testing datasets for predicting HIV (AUC = 0.72), syphilis (AUC = 0.75), gonorrhoea (AUC = 0.73), and chlamydia (AUC = 0.67) acquisition. Conclusions: Using machine learning techniques, our risk-prediction tool has acceptable reliability in predicting HIV/STI acquisition over the next 12 months. This tool may be used on clinic websites or digital health platforms to form part of an intervention tool to increase testing or reduce future HIV/STI risk.
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Affiliation(s)
- Xianglong Xu
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3053, Australia; (X.X.); (E.P.F.C.); (D.L.); (J.J.O.); (C.K.F.)
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia;
- China Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Centre, Xi’an 710061, China
| | - Zongyuan Ge
- Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, VIC 3800, Australia;
| | - Eric P. F. Chow
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3053, Australia; (X.X.); (E.P.F.C.); (D.L.); (J.J.O.); (C.K.F.)
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia;
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3053, Australia
| | - Zhen Yu
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia;
- Monash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, VIC 3800, Australia;
| | - David Lee
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3053, Australia; (X.X.); (E.P.F.C.); (D.L.); (J.J.O.); (C.K.F.)
| | - Jinrong Wu
- Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia;
| | - Jason J. Ong
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3053, Australia; (X.X.); (E.P.F.C.); (D.L.); (J.J.O.); (C.K.F.)
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia;
- China Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Centre, Xi’an 710061, China
| | - Christopher K. Fairley
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3053, Australia; (X.X.); (E.P.F.C.); (D.L.); (J.J.O.); (C.K.F.)
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia;
- China Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Centre, Xi’an 710061, China
| | - Lei Zhang
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3053, Australia; (X.X.); (E.P.F.C.); (D.L.); (J.J.O.); (C.K.F.)
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia;
- China Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Centre, Xi’an 710061, China
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
- Correspondence:
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John Cremin C, Dash S, Huang X. Big Data: Historic Advances and Emerging Trends in Biomedical Research. CURRENT RESEARCH IN BIOTECHNOLOGY 2022. [DOI: 10.1016/j.crbiot.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:8459-8486. [PMID: 35039756 PMCID: PMC8754556 DOI: 10.1007/s12652-021-03612-z] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/18/2021] [Indexed: 05/03/2023]
Abstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India
| | | | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea
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Sharma R, Anand H, Badr Y, Qiu RG. Time-to-event prediction using survival analysis methods for Alzheimer's disease progression. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12229. [PMID: 35005207 PMCID: PMC8719343 DOI: 10.1002/trc2.12229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/14/2021] [Accepted: 11/15/2021] [Indexed: 11/15/2022]
Abstract
INTRODUCTION Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time. METHODOLOGIES We simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017. RESULTS The experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval. CONCLUSIONS The proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.
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Affiliation(s)
- Rahul Sharma
- The Pennsylvania State UniversityMalvernPennsylvaniaUSA
| | - Harsh Anand
- The Pennsylvania State UniversityMalvernPennsylvaniaUSA
| | - Youakim Badr
- The Pennsylvania State UniversityMalvernPennsylvaniaUSA
| | - Robin G. Qiu
- The Pennsylvania State UniversityMalvernPennsylvaniaUSA
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James C, Ranson JM, Everson R, Llewellyn DJ. Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients. JAMA Netw Open 2021; 4:e2136553. [PMID: 34913981 PMCID: PMC8678688 DOI: 10.1001/jamanetworkopen.2021.36553] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
IMPORTANCE Machine learning algorithms could be used as the basis for clinical decision-making aids to enhance clinical practice. OBJECTIVE To assess the ability of machine learning algorithms to predict dementia incidence within 2 years compared with existing models and determine the optimal analytic approach and number of variables required. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used data from a prospective cohort of 15 307 participants without dementia at baseline to perform a secondary analysis of factors that could be used to predict dementia incidence. Participants attended National Alzheimer Coordinating Center memory clinics across the United States between 2005 and 2015. Analyses were conducted from March to May 2021. EXPOSURES 258 variables spanning domains of dementia-related clinical measures and risk factors. MAIN OUTCOMES AND MEASURES The main outcome was incident all-cause dementia diagnosed within 2 years of baseline assessment. RESULTS In a sample of 15 307 participants (mean [SD] age, 72.3 [9.8] years; 9129 [60%] women and 6178 [40%] men) without dementia at baseline, 1568 (10%) received a diagnosis of dementia within 2 years of their initial assessment. Compared with 2 existing models for dementia risk prediction (ie, Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score, and the Brief Dementia Screening Indicator), machine learning algorithms were superior in predicting incident all-cause dementia within 2 years. The gradient-boosted trees algorithm had a mean (SD) overall accuracy of 92% (1%), sensitivity of 0.45 (0.05), specificity of 0.97 (0.01), and area under the curve of 0.92 (0.01) using all 258 variables. Analysis of variable importance showed that only 6 variables were required for machine learning algorithms to achieve an accuracy of 91% and area under the curve of at least 0.89. Machine learning algorithms also identified up to 84% of participants who received an initial dementia diagnosis that was subsequently reversed to mild cognitive impairment or cognitively unimpaired, suggesting possible misdiagnosis. CONCLUSIONS AND RELEVANCE These findings suggest that machine learning algorithms could accurately predict incident dementia within 2 years in patients receiving care at memory clinics using only 6 variables. These findings could be used to inform the development and validation of decision-making aids in memory clinics.
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Affiliation(s)
- Charlotte James
- University of Exeter Medical School, Exeter, United Kingdom
- Deep Dementia Phenotyping Network, United Kingdom
| | - Janice M. Ranson
- University of Exeter Medical School, Exeter, United Kingdom
- Deep Dementia Phenotyping Network, United Kingdom
| | - Richard Everson
- Deep Dementia Phenotyping Network, United Kingdom
- Department of Computer Science, University of Exeter, Exeter, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| | - David J. Llewellyn
- University of Exeter Medical School, Exeter, United Kingdom
- Deep Dementia Phenotyping Network, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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Chowdhury M, Cervantes EG, Chan WY, Seitz DP. Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review. Front Psychiatry 2021; 12:738466. [PMID: 34616322 PMCID: PMC8488098 DOI: 10.3389/fpsyt.2021.738466] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 08/26/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health. Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist. Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved. Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.
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Affiliation(s)
- Mohammad Chowdhury
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Eddie Gasca Cervantes
- Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada
| | - Wai-Yip Chan
- Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada
| | - Dallas P. Seitz
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Medical Health Records-Based Mild Cognitive Impairment (MCI) Prediction for Effective Dementia Care. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18179223. [PMID: 34501812 PMCID: PMC8431613 DOI: 10.3390/ijerph18179223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022]
Abstract
Dementia is a cognitive impairment that poses a global threat. Current dementia treatments slow the progression of the disease. The timing of starting such treatment markedly affects the effectiveness of the treatment. Some experts mentioned that the optimal timing for starting the currently available treatment in order to delay progression to dementia is the mild cognitive impairment stage, which is the prior stage of dementia. However, medical records are typically only available at a later stage, i.e., from the early or middle stage of dementia. In order to address this limitation, this study developed a model using national health information data from 5 years prior, to predict dementia development 5 years in the future. The Senior Cohort Database, comprising 550,000 samples, were used for model development. The F-measure of the model predicting dementia development after a 5-year incubation period was 77.38%. Models for a 1- and 3-year incubation period were also developed for comparative analysis of dementia risk factors. The three models had some risk factors in common, but also had unique risk factors, depending on the stage. For the common risk factors, a difference in disease severity was confirmed. These findings indicate that the diagnostic criteria and treatment strategy for dementia should differ depending on the timing. Furthermore, since the results of this study present new dementia risk factors that have not been reported previously, this study may also contribute to identification of new dementia risk factors.
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Kalafatis C, Modarres MH, Apostolou P, Marefat H, Khanbagi M, Karimi H, Vahabi Z, Aarsland D, Khaligh-Razavi SM. Validity and Cultural Generalisability of a 5-Minute AI-Based, Computerised Cognitive Assessment in Mild Cognitive Impairment and Alzheimer's Dementia. Front Psychiatry 2021; 12:706695. [PMID: 34366938 PMCID: PMC8339427 DOI: 10.3389/fpsyt.2021.706695] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 06/17/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Early detection and monitoring of mild cognitive impairment (MCI) and Alzheimer's Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. We developed the Integrated Cognitive Assessment (ICA); a 5-min, language independent computerised cognitive test that employs an Artificial Intelligence (AI) model to improve its accuracy in detecting cognitive impairment. In this study, we aimed to evaluate the generalisability of the ICA in detecting cognitive impairment in MCI and mild AD patients. Methods: We studied the ICA in 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, Montreal Cognitive Assessment (MoCA) and Addenbrooke's Cognitive Examination (ACE) cognitive tests. Results: The ICA demonstrated convergent validity with MoCA (Pearson r=0.58, p<0.0001) and ACE (r=0.62, p<0.0001). The ICA AI model was able to detect cognitive impairment with an AUC of 81% for MCI patients, and 88% for mild AD patients. The AI model demonstrated improved performance with increased training data and showed generalisability in performance from one population to another. The ICA correlation of 0.17 (p = 0.01) with education years is considerably smaller than that of MoCA (r = 0.34, p < 0.0001) and ACE (r = 0.41, p < 0.0001) which displayed significant correlations. In a separate study the ICA demonstrated no significant practise effect over the duration of the study. Discussion: The ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large-scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture, and education.
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Affiliation(s)
- Chris Kalafatis
- Cognetivity Ltd, London, United Kingdom
- South London & Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Old Age Psychiatry, King's College London, London, United Kingdom
| | | | | | - Haniye Marefat
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mahdiyeh Khanbagi
- Department of Stem Cells and Developmental Biology, Cell Science Research Centre, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Hamed Karimi
- Department of Stem Cells and Developmental Biology, Cell Science Research Centre, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Zahra Vahabi
- Tehran University of Medical Sciences, Tehran, Iran
| | - Dag Aarsland
- Department of Old Age Psychiatry, King's College London, London, United Kingdom
| | - Seyed-Mahdi Khaligh-Razavi
- Cognetivity Ltd, London, United Kingdom
- Department of Stem Cells and Developmental Biology, Cell Science Research Centre, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
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Shimoda A, Li Y, Hayashi H, Kondo N. Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model. PLoS One 2021; 16:e0253988. [PMID: 34260593 PMCID: PMC8279312 DOI: 10.1371/journal.pone.0253988] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/16/2021] [Indexed: 12/16/2022] Open
Abstract
Due to difficulty in early diagnosis of Alzheimer's disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files' predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794-0.931), 0.882 (95% CI: 0.840-0.924), and 0.893 (95%CI: 0.832-0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000-1.000), 1.000 (95%CI: 1.000-1.000), 0.972 (95%CI: 0.918-1.000) and 0.917 (95%CI: 0.918-1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.
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Affiliation(s)
- Akihiro Shimoda
- Department of Public Health, McCann Healthcare Worldwide Japan Inc., Tokyo, Japan
| | - Yue Li
- Department of Public Health, McCann Healthcare Worldwide Japan Inc., Tokyo, Japan
| | - Hana Hayashi
- Department of Public Health, McCann Healthcare Worldwide Japan Inc., Tokyo, Japan
- Department of Global Health Promotion, Tokyo Medical and Dental University, Tokyo, Japan
- Graduate School of Health Management, Keio University, Tokyo, Japan
| | - Naoki Kondo
- Department of Social Epidemiology and Global Health, Graduate School of Medicine and School of Public Health, Kyoto University, Kyoto, Japan
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Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA Open 2021; 4:ooab052. [PMID: 34350389 PMCID: PMC8327375 DOI: 10.1093/jamiaopen/ooab052] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/21/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
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Affiliation(s)
- Sayantan Kumar
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Inez Oh
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Suzanne Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
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Artificial intelligence in drug design: algorithms, applications, challenges and ethics. FUTURE DRUG DISCOVERY 2021. [DOI: 10.4155/fdd-2020-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.
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Linden T, De Jong J, Lu C, Kiri V, Haeffs K, Fröhlich H. An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data. Front Artif Intell 2021; 4:610197. [PMID: 34095818 PMCID: PMC8176093 DOI: 10.3389/frai.2021.610197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 04/21/2021] [Indexed: 01/16/2023] Open
Abstract
Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities (e.g., anxiety, migraine, and stroke). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient-specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for individual epilepsy patients. In this work, we use inpatient and outpatient administrative health claims data of around 19,500 U.S. epilepsy patients. We suggest a dedicated multimodal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model-DeepLORI) to predict the time-dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods. Moreover, we show that DeepLORI-based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.
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Affiliation(s)
- Thomas Linden
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
- UCB Biosciences GmbH, Monheim, Germany
| | | | - Chao Lu
- UCB Ltd., Raleigh, NC, United States
| | | | | | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
- UCB Biosciences GmbH, Monheim, Germany
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A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18105386. [PMID: 34070100 PMCID: PMC8158341 DOI: 10.3390/ijerph18105386] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 05/12/2021] [Accepted: 05/13/2021] [Indexed: 01/14/2023]
Abstract
The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.
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Cecula P, Yu J, Dawoodbhoy FM, Delaney J, Tan J, Peacock I, Cox B. Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review. Heliyon 2021; 7:e06626. [PMID: 33898804 PMCID: PMC8060579 DOI: 10.1016/j.heliyon.2021.e06626] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/20/2021] [Accepted: 03/24/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research. METHODS The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria. RESEARCH 3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face. CONCLUSION Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.
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Affiliation(s)
- Paulina Cecula
- Imperial College London Business School, London, UK
- Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UK
| | - Jiakun Yu
- Imperial College London Business School, London, UK
- Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UK
| | - Fatema Mustansir Dawoodbhoy
- Imperial College London Business School, London, UK
- Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UK
| | - Jack Delaney
- Imperial College London Business School, London, UK
- Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UK
| | - Joseph Tan
- Imperial College London Business School, London, UK
- Brighton and Sussex Medical School, Brighton, East Sussex, BN1 9PX, UK
| | - Iain Peacock
- Imperial College London Business School, London, UK
- Brighton and Sussex Medical School, Brighton, East Sussex, BN1 9PX, UK
| | - Benita Cox
- Imperial College London Business School, London, UK
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Wedlund L, Kvedar J, Layman W. Anticipating and treating dementia: lessons hidden in plain sight. NPJ Digit Med 2020; 3:153. [PMID: 33299106 PMCID: PMC7680154 DOI: 10.1038/s41746-020-00359-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 10/23/2020] [Indexed: 11/22/2022] Open
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Shehzad A, Rockwood K, Stanley J, Dunn T, Howlett SE. Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach. J Med Internet Res 2020; 22:e20840. [PMID: 33174853 PMCID: PMC7688393 DOI: 10.2196/20840] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 08/17/2020] [Accepted: 10/24/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with dementia. The value of such data are enhanced when the specific dementia stage is identified. OBJECTIVE We aimed to develop a supervised machine learning algorithm to classify dementia stages based on tracked symptoms. METHODS We employed clinical data from 717 people from 3 sources: (1) a memory clinic; (2) long-term care; and (3) an open-label trial of donepezil in vascular and mixed dementia (VASPECT). Symptoms were captured with SymptomGuide Dementia. A clinician classified participants into 4 groups using either the Functional Assessment Staging Test or the Global Deterioration Scale as mild cognitive impairment, mild dementia, moderate dementia, or severe dementia. Individualized symptom profiles from the pooled data were used to train machine learning models to predict dementia severity. Models trained with 6 different machine learning algorithms were compared using nested cross-validation to identify the best performing model. Model performance was assessed using measures of balanced accuracy, precision, recall, Cohen κ, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). The best performing algorithm was used to train a model optimized for balanced accuracy. RESULTS The study population was mostly female (424/717, 59.1%), older adults (mean 77.3 years, SD 10.6, range 40-100) with mild to moderate dementia (332/717, 46.3%). Age, duration of symptoms, 37 unique dementia symptoms, and 10 symptom-derived variables were used to distinguish dementia stages. A model trained with a support vector machine learning algorithm using a one-versus-rest approach showed the best performance. The correct dementia stage was identified with 83% balanced accuracy (Cohen κ=0.81, AUPRC 0.91, AUROC 0.96). The best performance was seen when classifying severe dementia (AUROC 0.99). CONCLUSIONS A supervised machine learning algorithm exhibited excellent performance in identifying dementia stages based on dementia symptoms reported in an online environment. This novel dementia staging algorithm can be used to describe dementia stage based on user-reported symptoms. This type of symptom recording offers real-world data that reflect important symptoms in people with dementia.
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Affiliation(s)
| | - Kenneth Rockwood
- DGI Clinical Inc, Halifax, NS, Canada.,Geriatric Medicine Research Unit, Nova Scotia Health Authority, Halifax, NS, Canada.,Division of Geriatric Medicine, Dalhousie University, Halifax, NS, Canada
| | | | | | - Susan E Howlett
- DGI Clinical Inc, Halifax, NS, Canada.,Division of Geriatric Medicine, Dalhousie University, Halifax, NS, Canada.,Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
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Xu J, Wang F, Xu Z, Adekkanattu P, Brandt P, Jiang G, Kiefer RC, Luo Y, Mao C, Pacheco JA, Rasmussen LV, Zhang Y, Isaacson R, Pathak J. Data-driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records. Learn Health Syst 2020; 4:e10246. [PMID: 33083543 PMCID: PMC7556420 DOI: 10.1002/lrh2.10246] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 07/19/2020] [Accepted: 08/06/2020] [Indexed: 12/04/2022] Open
Abstract
Introduction We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes. Methods A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi‐specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data. The other 6795 patients, labeled as controls, were matched on age and gender with the cases and randomly selected in the ratio of 9:1. Prediction models with multiple ML algorithms were trained on this cohort using 5‐fold cross‐validation. XGBoost was used to rank the variable importance. Results Four subphenotypes were computationally derived. Subphenotype A (n = 273; 28.2%) had more patients with cardiovascular diseases; subphenotype B (n = 221; 27.9%) had more patients with mental health illnesses, such as depression and anxiety; patients in subphenotype C (n = 183; 23.1%) were overall older (mean (SD) age, 79.5 (5.4) years) and had the most comorbidities including diabetes, cardiovascular diseases, and mental health disorders; and subphenotype D (n = 115; 14.5%) included patients who took anti‐dementia drugs and had sensory problems, such as deafness and hearing impairment. The 0‐year prediction model for AD risk achieved an area under the receiver operating curve (AUC) of 0.764 (SD: 0.02); the 6‐month model, 0.751 (SD: 0.02); the 1‐year model, 0.752 (SD: 0.02); the 2‐year model, 0.749 (SD: 0.03); and the 3‐year model, 0.735 (SD: 0.03), respectively. Based on variable importance, the top‐ranked comorbidities included depression, stroke/transient ischemic attack, hypertension, anxiety, mobility impairments, and atrial fibrillation. The top‐ranked medications included anti‐dementia drugs, antipsychotics, antiepileptics, and antidepressants. Conclusions Four subphenotypes were computationally derived that correlated with cardiovascular diseases and mental health illnesses. ML algorithms based on patient demographics, diagnosis, and treatment demonstrated promising results in predicting the risk of developing AD at different time points across an individual's lifespan.
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Affiliation(s)
- Jie Xu
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Fei Wang
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Zhenxing Xu
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Prakash Adekkanattu
- Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Pascal Brandt
- Biomedical Informatics and Medical Education University of Washington Seattle Washington USA
| | - Guoqian Jiang
- Department of Health Sciences Research Mayo Clinic Rochester Minnesota USA
| | - Richard C Kiefer
- Department of Health Sciences Research Mayo Clinic Rochester Minnesota USA
| | - Yuan Luo
- Feinberg School of Medicine Northwestern University Chicago Illinois USA
| | - Chengsheng Mao
- Feinberg School of Medicine Northwestern University Chicago Illinois USA
| | - Jennifer A Pacheco
- Feinberg School of Medicine Northwestern University Chicago Illinois USA
| | - Luke V Rasmussen
- Feinberg School of Medicine Northwestern University Chicago Illinois USA
| | - Yiye Zhang
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Richard Isaacson
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Jyotishman Pathak
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
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Artificial and Internet of Healthcare Things Based Alzheimer Care During COVID 19. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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