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Jabarin A, Shtar G, Feinshtein V, Mazuz E, Shapira B, Ben-Shabat S, Rokach L. Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model. Brief Bioinform 2024; 25:bbae108. [PMID: 38647152 PMCID: PMC11033730 DOI: 10.1093/bib/bbae108] [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: 12/06/2023] [Revised: 02/04/2024] [Accepted: 02/25/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leading causes of cancer-related deaths worldwide. The lack of clear treatment directions underscores the urgent need for innovative approaches to address and manage this deadly condition. In this research, we repurpose drugs with potential anti-cancer activity using machine learning (ML). METHODS We tackle the problem by using a neural network trained on drug-target interaction information enriched with drug-drug interaction information, which has not been used for anti-cancer drug repurposing before. We focus on eravacycline, an antibacterial drug, which was selected and evaluated to assess its anti-cancer effects. RESULTS Eravacycline significantly inhibited the proliferation and migration of BxPC-3 cells and induced apoptosis. CONCLUSION Our study highlights the potential of drug repurposing for cancer treatment using ML. Eravacycline showed promising results in inhibiting cancer cell proliferation, migration and inducing apoptosis in PDAC. These findings demonstrate that our developed ML drug repurposing models can be applied to a wide range of new oncology therapeutics, to identify potential anti-cancer agents. This highlights the potential and presents a promising approach for identifying new therapeutic options.
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Affiliation(s)
- Adi Jabarin
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev (BGU), P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Guy Shtar
- Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Valeria Feinshtein
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev (BGU), P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Eyal Mazuz
- Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Bracha Shapira
- Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Shimon Ben-Shabat
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev (BGU), P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Lior Rokach
- Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel
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Terranova C, Cestonaro C, Fava L, Cinquetti A. AI and professional liability assessment in healthcare. A revolution in legal medicine? Front Med (Lausanne) 2024; 10:1337335. [PMID: 38259835 PMCID: PMC10800912 DOI: 10.3389/fmed.2023.1337335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
The adoption of advanced artificial intelligence (AI) systems in healthcare is transforming the healthcare-delivery landscape. Artificial intelligence may enhance patient safety and improve healthcare outcomes, but it presents notable ethical and legal dilemmas. Moreover, as AI streamlines the analysis of the multitude of factors relevant to malpractice claims, including informed consent, adherence to standards of care, and causation, the evaluation of professional liability might also benefit from its use. Beginning with an analysis of the basic steps in assessing professional liability, this article examines the potential new medical-legal issues that an expert witness may encounter when analyzing malpractice cases and the potential integration of AI in this context. These changes related to the use of integrated AI, will necessitate efforts on the part of judges, experts, and clinicians, and may require new legislative regulations. A new expert witness will be likely necessary in the evaluation of professional liability cases. On the one hand, artificial intelligence will support the expert witness; however, on the other hand, it will introduce specific elements into the activities of healthcare workers. These elements will necessitate an expert witness with a specialized cultural background. Examining the steps of professional liability assessment indicates that the likely path for AI in legal medicine involves its role as a collaborative and integrated tool. The combination of AI with human judgment in these assessments can enhance comprehensiveness and fairness. However, it is imperative to adopt a cautious and balanced approach to prevent complete automation in this field.
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Affiliation(s)
- Claudio Terranova
- Legal Medicine and Toxicology, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua, Italy
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Khorshidi HA, Marshall D, Goranitis I, Schroeder B, IJzerman M. System dynamics simulation for evaluating implementation strategies of genomic sequencing: tutorial and conceptual model. Expert Rev Pharmacoecon Outcomes Res 2024; 24:37-47. [PMID: 37803528 DOI: 10.1080/14737167.2023.2267764] [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: 04/21/2023] [Accepted: 10/03/2023] [Indexed: 10/08/2023]
Abstract
INTRODUCTION Precision Medicine (PM), especially in oncology, involve diagnostic and complex treatment pathways that are based on genomic features. To conduct evaluation and decision analysis for PM, advanced modeling techniques are needed due to its complexity. Although System Dynamics (SD) has strong modeling power, it has not been widely used in PM and individualized treatment. AREAS COVERED We explained SD tools using examples in cancer context and the rationale behind using SD for genomic testing and personalized oncology. We compared SD with other Dynamic Simulation Modelling (DSM) methods and listed SD's advantages. We developed a conceptual model using Causal Loop Diagram (CLD) for strategic decision-making in Whole Genome Sequencing (WGS) implementation. EXPERT OPINION The paper demonstrates that SD is well-suited for health policy evaluation challenges and has useful tools for modeling precision oncology and genomic testing. SD's system-oriented modeling captures dynamic and complex interactions within systems using feedback loops. SD models are simple to implement, utilize less data and computational resources, and conduct both exploratory and explanatory analyses over time. If the targeted system has complex interactions and many components, deals with lack of data, and requires interpretability and clinicians' input, SD offers attractive advantages for modeling and evaluating scenarios.
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Affiliation(s)
- Hadi A Khorshidi
- Cancer Health Services Research, University of Melbourne Centre for Cancer Research, Parkville, Australia
- School of Computing and Information Systems, University of Melbourne, Carlton, Australia
- ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), Carlton, Australia
| | - Deborah Marshall
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Ilias Goranitis
- Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, Centre for Health Policy, Carlton, Australia
| | | | - Maarten IJzerman
- Cancer Health Services Research, University of Melbourne Centre for Cancer Research, Parkville, Australia
- Erasmus School of Health Policy & Management, Department Health Services Management & Organisation, Rotterdam, the Netherlands
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Kim JK, McCammon KA, Kim KJ, Rickard M, Lorenzo AJ, Chua ME. Development and use of machine learning models for prediction of male sling success A proof-of-concept institutional evaluation. Can Urol Assoc J 2023; 17:E309-E314. [PMID: 37494315 PMCID: PMC10581726 DOI: 10.5489/cuaj.8265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
INTRODUCTION For mild to moderate male stress urinary incontinence (SUI), transobturator male slings remain an effective option for management. We aimed to use a machine learning (ML )-based model to predict those who will have a long-term success in managing SUI with male sling. METHODS All transobturator male sling cases from August 2006 to June 2012 by a single surgeon were reviewed. Outcome of interest was defined as 'cure': complete dryness with 0 pads used, without the need for additional procedures. Clinical variables included in ML models were: number of pads used daily, age, height, weight, race, incontinence type, etiology of incontinence, history of radiation, smoking, bladder neck contracture, and prostatectomy. Model performance was assessed using area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1-score. RESULTS A total of 181 patients were included in the model. The mean followup was 56.4 months (standard deviation [SD ] 41.6). Slightly more than half (53.6%, 97/181) of patients had procedural success. Logistic regression, K-nearest neighbor (KNN ), naive Bayes, decision tree, and random forest models were developed using ML. KNN model had the best performance, with AUROC of 0.759, AUPRC of 0.916, and F1-score of 0.833. Following ensemble learning with bagging and calibration, KNN model was further improved, with AUROC of 0.821, AUPRC of 0.921, and F-1 score of 0.848. CONCLUSIONS ML-based prediction of long-term transobturator male sling is feasible. The low numbers of patients used to develop the model prompt further validation and development of the model but may serve as a decision-making aid for practitioners in the future.
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Affiliation(s)
- Jin K. Kim
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Kurt A. McCammon
- Department of Urology, Eastern Virginia Medical School, Norfolk, VA; Devine-Jordan Center for Reconstructive Surgery and Pelvic Health, Urology of Virginia, Virginia Beach, VA, United States
| | - Kellie J. Kim
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mandy Rickard
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Armando J. Lorenzo
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Michael E. Chua
- Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada
- Institute of Urology, St. Luke’s Medical Center, Quezon City, Philippines
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Xue P, Si M, Qin D, Wei B, Seery S, Ye Z, Chen M, Wang S, Song C, Zhang B, Ding M, Zhang W, Bai A, Yan H, Dang L, Zhao Y, Rezhake R, Zhang S, Qiao Y, Qu Y, Jiang Y. Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis. J Med Internet Res 2023; 25:e43832. [PMID: 36862499 PMCID: PMC10020907 DOI: 10.2196/43832] [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: 10/26/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.
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Affiliation(s)
- Peng Xue
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyu Si
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongxu Qin
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bingrui Wei
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | - Zichen Ye
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyang Chen
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sumeng Wang
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cheng Song
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Ding
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenling Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Anying Bai
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huijiao Yan
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Le Dang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuqian Zhao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science & Technology of China, Sichuan, China
| | - Remila Rezhake
- Affiliated Cancer Hospital, The 3rd Affiliated Teaching Hospital of Xinjiang Medical University, Xinjiang, China
| | - Shaokai Zhang
- Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Henan, China
| | - Youlin Qiao
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yimin Qu
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Jansson M, Ohtonen P, Alalääkkölä T, Heikkinen J, Mäkiniemi M, Lahtinen S, Lahtela R, Ahonen M, Jämsä S, Liisantti J. Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities. BMC Health Serv Res 2022; 22:1513. [PMID: 36510176 PMCID: PMC9746075 DOI: 10.1186/s12913-022-08780-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/02/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) and machine learning are transforming the optimization of clinical and patient workflows in healthcare. There is a need for research to specify clinical requirements for AI-enhanced care pathway planning and scheduling systems to improve human-AI interaction in machine learning applications. The aim of this study was to assess content validity and prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. METHODS A prospective content validity assessment was conducted in five university hospitals in three different countries using an electronic survey. The content of the survey was formed from clinical requirements, which were formulated into generic statements of required AI functionalities. The relevancy of each statement was evaluated using a content validity index. In addition, weighted ranking points were calculated to prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. RESULTS A total of 50 responses were received from clinical professionals from three European countries. An item-level content validity index ranged from 0.42 to 0.96. 45% of the generic statements were considered good. The highest ranked functionalities for an AI-enhanced care pathway planning and scheduling system were related to risk assessment, patient profiling, and resources. The highest ranked functionalities for the user interface were related to the explainability of machine learning models. CONCLUSION This study provided a comprehensive list of functionalities that can be used to design future AI-enhanced solutions and evaluate the designed solutions against requirements. The relevance of statements concerning the AI functionalities were considered somewhat relevant, which might be due to the low level or organizational readiness for AI in healthcare.
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Affiliation(s)
- Miia Jansson
- grid.10858.340000 0001 0941 4873Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Pasi Ohtonen
- grid.10858.340000 0001 0941 4873Research Unit of Surgery, Anesthesia and Intensive Care, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Timo Alalääkkölä
- grid.412326.00000 0004 4685 4917Testing and Innovations, Oulu University Hospital, Oulu, Finland
| | - Juuso Heikkinen
- grid.412326.00000 0004 4685 4917Division of Orthopedic and Trauma Surgery, Department of Surgery, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Minna Mäkiniemi
- grid.412326.00000 0004 4685 4917Oulu University Hospital, Oulu, Finland
| | - Sanna Lahtinen
- grid.412326.00000 0004 4685 4917Department of Anesthesiology, Oulu University Hospital, Oulu, Finland ,MRC Oulu, Research Group of Anesthesiology, Oulu, Finland
| | - Riikka Lahtela
- grid.412326.00000 0004 4685 4917Department of Anesthesiology, Oulu University Hospital, Oulu, Finland
| | - Merja Ahonen
- grid.412326.00000 0004 4685 4917Department of Anesthesiology, Oulu University Hospital, Oulu, Finland ,MRC Oulu, Research Group of Anesthesiology, Oulu, Finland
| | - Sirpa Jämsä
- grid.412326.00000 0004 4685 4917Sense Organ Diseases Centre, Oulu University Hospital, Oulu, Finland
| | - Janne Liisantti
- grid.412326.00000 0004 4685 4917Department of Anesthesiology, Oulu University Hospital, Oulu, Finland ,MRC Oulu, Research Group of Anesthesiology, Oulu, Finland
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Russell S, Kumar A. Providing Care: Intrinsic Human-Machine Teams and Data. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1369. [PMID: 37420389 DOI: 10.3390/e24101369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 07/09/2023]
Abstract
Despite the many successes of artificial intelligence in healthcare applications where human-machine teaming is an intrinsic characteristic of the environment, there is little work that proposes methods for adapting quantitative health data-features with human expertise insights. A method for incorporating qualitative expert perspectives in machine learning training data is proposed. The method implements an entropy-based consensus construct that minimizes the challenges of qualitative-scale data such that they can be combined with quantitative measures in a critical clinical event (CCE) vector. Specifically, the CCE vector minimizes the effects where (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. The incorporation of human perspectives in machine learning training data provides encoding of human considerations in the subsequent machine learning model. This encoding provides a basis for increasing explainability, understandability, and ultimately trust in AI-based clinical decision support system (CDSS), thereby improving human-machine teaming concerns. A discussion of applying the CCE vector in a CDSS regime and implications for machine learning are also presented.
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Affiliation(s)
- Stephen Russell
- Department of Research, Opportunities and Innovation in Data Science, Jackson Health System, Miami, FL 33136, USA
| | - Ashwin Kumar
- Department of Research, Opportunities and Innovation in Data Science, Jackson Health System, Miami, FL 33136, USA
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Gottlieb ER, Samuel M, Bonventre JV, Celi LA, Mattie H. Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit. Adv Chronic Kidney Dis 2022; 29:431-438. [PMID: 36253026 PMCID: PMC9586459 DOI: 10.1053/j.ackd.2022.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/01/2022] [Accepted: 06/22/2022] [Indexed: 01/25/2023]
Abstract
Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.
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Affiliation(s)
- Eric R Gottlieb
- Renal Section, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA.
| | | | - Joseph V Bonventre
- Renal Section, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Leo A Celi
- Harvard Medical School, Boston, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; MIT Critical Data, Cambridge, MA; Harvard T.H. Chan School of Public Health, Boston, MA; Beth Israel Deaconess Medical Center, Boston, MA
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Antúnez-Muiños P, Vicente-Palacios V, Pérez-Sánchez P, Sampedro-Gómez J, Sánchez-Puente A, Dorado-Díaz PI, Nombela-Franco L, Salinas P, Gutiérrez-García H, Amat-Santos I, Peral V, Morcuende A, Asmarats L, Freixa X, Regueiro A, Caneiro-Queija B, Estevez-Loureiro R, Rodés-Cabau J, Sánchez PL, Cruz-González I. Predictive Power for Thrombus Detection after Atrial Appendage Closure: Machine Learning vs. Classical Methods. J Pers Med 2022; 12:jpm12091413. [PMID: 36143197 PMCID: PMC9503612 DOI: 10.3390/jpm12091413] [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: 06/14/2022] [Revised: 08/12/2022] [Accepted: 08/21/2022] [Indexed: 11/16/2022] Open
Abstract
Device-related thrombus (DRT) after left atrial appendage (LAA) closure is infrequent but correlates with an increased risk of thromboembolism. Therefore, the search for DRT predictors is a topic of interest. In the literature, multivariable methods have been used achieving non-consistent results, and to the best of our knowledge, machine learning techniques have not been used yet for thrombus detection after LAA occlusion. Our aim is to compare both methodologies with respect to predictive power and the search for predictors of DRT. To this end, a multicenter study including 1150 patients who underwent LAA closure was analyzed. Two lines of experiments were performed: with and without resampling. Multivariate and machine learning methodologies were applied to both lines. Predictive power and the extracted predictors for all experiments were gathered. ROC curves of 0.5446 and 0.7974 were obtained for multivariate analysis and machine learning without resampling, respectively. However, the resampling experiment showed no significant difference between them (0.52 vs. 0.53 ROC AUC). A difference between the predictors selected was observed, with the multivariable methodology being more stable. These results question the validity of predictors reported in previous studies and demonstrate their disparity. Furthermore, none of the techniques analyzed is superior to the other for these data.
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Affiliation(s)
- Pablo Antúnez-Muiños
- CIBERCV, University Hospital of Salamanca, 37007 Salamanca, Spain
- Biomedical Research Institute of Salamanca (IBSAL), 37007 Salamanca, Spain
- Correspondence: ; Tel.: +34-923291100
| | | | - Pablo Pérez-Sánchez
- CIBERCV, University Hospital of Salamanca, 37007 Salamanca, Spain
- Biomedical Research Institute of Salamanca (IBSAL), 37007 Salamanca, Spain
| | | | | | - Pedro Ignacio Dorado-Díaz
- CIBERCV, University Hospital of Salamanca, 37007 Salamanca, Spain
- Biomedical Research Institute of Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Luis Nombela-Franco
- Instituto Cardiovascular, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain
| | - Pablo Salinas
- Instituto Cardiovascular, Hospital Clínico San Carlos, IdISSC, 28040 Madrid, Spain
| | - Hipólito Gutiérrez-García
- CIBERCV, Instituto de Ciencias del Corazón (ICICOR), Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain
| | - Ignacio Amat-Santos
- CIBERCV, Instituto de Ciencias del Corazón (ICICOR), Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain
| | - Vicente Peral
- Department of Cardiology, Health Research Institute of the Balearic Islands (IdISBa), Hospital Universitari Son Espases, 07120 Palma, Spain
| | - Antonio Morcuende
- Department of Cardiology, Health Research Institute of the Balearic Islands (IdISBa), Hospital Universitari Son Espases, 07120 Palma, Spain
| | - Lluis Asmarats
- Quebec Heart and Kung Institute, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Xavier Freixa
- Institut Clínic Cardiovascular, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Ander Regueiro
- Institut Clínic Cardiovascular, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | | | | | - Josep Rodés-Cabau
- Quebec Heart and Kung Institute, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Pedro Luis Sánchez
- CIBERCV, University Hospital of Salamanca, 37007 Salamanca, Spain
- Biomedical Research Institute of Salamanca (IBSAL), 37007 Salamanca, Spain
| | - Ignacio Cruz-González
- CIBERCV, University Hospital of Salamanca, 37007 Salamanca, Spain
- Biomedical Research Institute of Salamanca (IBSAL), 37007 Salamanca, Spain
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Lohiniva AL, Nurzhynska A, Hudi AH, Anim B, Aboagye DC. Infodemic Management Using Digital Information and Knowledge Cocreation to Address COVID-19 Vaccine Hesitancy: Case Study From Ghana. JMIR INFODEMIOLOGY 2022; 2:e37134. [PMID: 35854815 PMCID: PMC9281514 DOI: 10.2196/37134] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/24/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Infodemic management is an integral part of pandemic management. Ghana Health Services (GHS) together with the UNICEF (United Nations International Children's Emergency Fund) Country Office have developed a systematic process that effectively identifies, analyzes, and responds to COVID-19 and vaccine-related misinformation in Ghana. OBJECTIVE This paper describes an infodemic management system workflow based on digital data collection, qualitative methodology, and human-centered systems to support the COVID-19 vaccine rollout in Ghana with examples of system implementation. METHODS The infodemic management system was developed by the Health Promotion Division of the GHS and the UNICEF Country Office. It uses Talkwalker, a social listening software platform, to collect misinformation on the web. The methodology relies on qualitative data analysis and interpretation as well as knowledge cocreation to verify the findings. RESULTS A multi-sectoral National Misinformation Task Force was established to implement and oversee the misinformation management system. Two members of the task force were responsible for carrying out the analysis. They used Talkwalker to find posts that include the keywords related to COVID-19 vaccine-related discussions. They then assessed the significance of the posts on the basis of the engagement rate and potential reach of the posts, negative sentiments, and contextual factors. The process continues by identifying misinformation within the posts, rating the risk of identified misinformation posts, and developing proposed responses to address them. The results of the analysis are shared weekly with the Misinformation Task Force for their review and verification to ensure that the risk assessment and responses are feasible, practical, and acceptable in the context of Ghana. CONCLUSIONS The paper describes an infodemic management system workflow in Ghana based on qualitative data synthesis that can be used to manage real-time infodemic responses.
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Affiliation(s)
| | | | | | - Bridget Anim
- Health Promotion Division Ghana Health Services Accra Ghana
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Chopannejad S, Sadoughi F, Bagherzadeh R, Shekarchi S. Predicting major adverse cardiovascular events in acute coronary syndrome: A scoping review of machine learning approaches. Appl Clin Inform 2022; 13:720-740. [PMID: 35617971 PMCID: PMC9329142 DOI: 10.1055/a-1863-1589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. OBJECTIVE The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. METHODS In order to predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The findings of the studies are presented in the form of a narrative synthesis of evidence. RESULTS According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N=20) achieved a high Area under the ROC Curve between 0.8 to 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. CONCLUSION Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.
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Affiliation(s)
- Sara Chopannejad
- Student Research Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Farahnaz Sadoughi
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Rafat Bagherzadeh
- English Language Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Sakineh Shekarchi
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
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Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions. J Eat Disord 2022; 10:66. [PMID: 35527306 PMCID: PMC9080128 DOI: 10.1186/s40337-022-00581-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 04/17/2022] [Indexed: 12/02/2022] Open
Abstract
Advances in machine learning and digital data provide vast potential for mental health predictions. However, research using machine learning in the field of eating disorders is just beginning to emerge. This paper provides a narrative review of existing research and explores potential benefits, limitations, and ethical considerations of using machine learning to aid in the detection, prevention, and treatment of eating disorders. Current research primarily uses machine learning to predict eating disorder status from females' responses to validated surveys, social media posts, or neuroimaging data often with relatively high levels of accuracy. This early work provides evidence for the potential of machine learning to improve current eating disorder screening methods. However, the ability of these algorithms to generalise to other samples or be used on a mass scale is only beginning to be explored. One key benefit of machine learning over traditional statistical methods is the ability of machine learning to simultaneously examine large numbers (100s to 1000s) of multimodal predictors and their complex non-linear interactions, but few studies have explored this potential in the field of eating disorders. Machine learning is also being used to develop chatbots to provide psychoeducation and coping skills training around body image and eating disorders, with implications for early intervention. The use of machine learning to personalise treatment options, provide ecological momentary interventions, and aid the work of clinicians is also discussed. Machine learning provides vast potential for the accurate, rapid, and cost-effective detection, prevention, and treatment of eating disorders. More research is needed with large samples of diverse participants to ensure that machine learning models are accurate, unbiased, and generalisable to all people with eating disorders. There are important limitations and ethical considerations with utilising machine learning methods in practice. Thus, rather than a magical solution, machine learning should be seen as an important tool to aid the work of researchers, and eventually clinicians, in the early identification, prevention, and treatment of eating disorders.
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