This paper is in the following e-collection/theme issue:

Stroke (211) Clinical Information and Decision Making (1736) Machine Learning (1840)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47736, first published .
Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study

Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study

Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study

Authors of this article:

Sermkiat Lolak1 Author Orcid Image ;   John Attia2 Author Orcid Image ;   Gareth J McKay3 Author Orcid Image ;   Ammarin Thakkinstian1 Author Orcid Image
Article Authors Cited by (12) Tweetations (2) Metrics

Journals

  1. Abujaber A, Albalkhi I, Imam Y, Nashwan A, Yaseen S, Akhtar N, Alkhawaldeh I. Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning. Journal of Personalized Medicine 2023;13(11):1555 View
  2. Bui H, Nguyễn Thị Phương Q, Cam Tu H, Nguyen Phuong S, Pham T, Vu T, Nguyen Thi Thu H, Khanh Ho L, Nguyen Tien D. The Roles of NOTCH3 p.R544C and Thrombophilia Genes in Vietnamese Patients With Ischemic Stroke: Study Involving a Hierarchical Cluster Analysis. JMIR Bioinformatics and Biotechnology 2024;5:e56884 View
  3. Inchingolo F, Inchingolo A, Piras F, Ferrante L, Mancini A, Palermo A, Inchingolo A, Dipalma G. Management of Patients Receiving Anticoagulation Therapy in Dental Practice: A Systematic Review. Healthcare 2024;12(15):1537 View
  4. Zhou Y, Xie H, Li X, Huang W, Wu X, Zhang X, Dou Z, Li Z, Hou W, Chen L. Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training. Journal of NeuroEngineering and Rehabilitation 2024;21(1) View
  5. Wang B, Li D, Geng Y, Jin F, Wang Y, Lv C, Lv T, Xue Y, Zhang P. Interpretable predictive value of including HDL-2b and HDL-3 in an explainable boosting machine model for multiclass classification of coronary artery stenosis severity in acute myocardial infarction patients. European Heart Journal - Digital Health 2025;6(2):228 View
  6. Gao Y, Myers S, Chen S, Dligach D, Miller T, Bitterman D, Chen G, Mayampurath A, Churpek M, Afshar M. Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability. JAMIA Open 2024;8(1) View
  7. Heseltine-Carp W, Courtman M, Browning D, Kasabe A, Allen M, Streeter A, Ifeachor E, James M, Mullin S. Machine learning to predict stroke risk from routine hospital data: A systematic review. International Journal of Medical Informatics 2025;196:105811 View
  8. Liu H, Kou W, Wu Y, Hing Chau P, Chung T, Fong D. Predicting Childhood and Adolescence Hypertension: Analysis of Predictors Using Machine Learning. Pediatrics 2025;155(3) View
  9. Zhu J, Lin L, Si L, Zhao H, Song H, Xu X. Urban and rural disparities in stroke prediction using machine learning among Chinese older adults. Scientific Reports 2025;15(1) View
  10. Zimmerman R, Hernandez E, Tristani-Firouzi M, Yandell M, Steinberg B. Explainable artificial intelligence for stroke risk stratification in atrial fibrillation. European Heart Journal - Digital Health 2025;6(3):317 View
  11. Basem J, Mani R, Sun S, Gilotra K, Dianati-Maleki N, Dashti R. Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review. Frontiers in Cardiovascular Medicine 2025;12 View

Conference Proceedings

  1. S I, Pillai A. 2024 International Conference on Inventive Computation Technologies (ICICT). Explainable Artificial Intelligence in Healthcare -A Review View