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

Machine Learning (2017) Responsible Health AI (39) Myocardial Infarction (Heart Attack) (56) Stroke (231) Artificial Intelligence in Diabetes Care and Prevention (49) Decision Support for Health Professionals (1499) Clinical Information and Decision Making (1858)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66200, first published .
A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study

A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study

A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study

Authors of this article:

Yang Yang1 Author Orcid Image ;   Che-Yi Liao1 Author Orcid Image ;   Esmaeil Keyvanshokooh2 Author Orcid Image ;   Hui Shao3 Author Orcid Image ;   Mary Beth Weber3 Author Orcid Image ;   Francisco J Pasquel3, 4 Author Orcid Image ;   Gian-Gabriel P Garcia1 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Parab R, Feeley J, Valero M, Chadalawada L, Garcia G, Kar S, Madabhushi A, Breton M, Li J, Shao H, Pasquel F. Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead. Endocrine Practice 2025 View
  2. Cao J, Keyvanshokooh E, Liu T. Safe Reinforcement Learning with Contextual Information: Theory and Application to Personalized Comorbidity Management. SSRN Electronic Journal 2023 View