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

Stroke (166) Clinical Information and Decision Making (1434) Machine Learning (1501)

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 (4) Tweetations (2) Metrics

Sermkiat Lolak   1 , MD ;   John Attia   2 , MD, PhD ;   Gareth J McKay   3 , MD, PhD ;   Ammarin Thakkinstian   1 , PhD

1 Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand

2 Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, Hunter Medical Research Institute, University of Newcastle, New South Wales, Australia

3 Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom

Corresponding Author:

  • Ammarin Thakkinstian, PhD
  • Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine
  • Ramathibodi Hospital
  • Mahidol University
  • 4th Floor, Sukho Place Building
  • 218/11 Sukhothai Road, Suan Chitlada, Dusit
  • Bangkok, 10300
  • Thailand
  • Phone: 66 2-201-1269
  • Email: ammarin.tha@mahidol.edu