Published on in Vol 7 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54872, first published .
Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study

Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study

Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study

Journals

  1. Hashimoto Y, Inoue N, Tani T, Imai S. Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study. JMIR Aging 2025;8:e65898 View
  2. Zhang Z, Duan Y, Wang Y, Gao Z. Multivariable risk prediction models for postoperative cardiac injury in adults undergoing non-cardiac surgery: a systematic review and meta-analysis protocol. BMJ Open 2025;15(10):e108282 View
  3. Yang X, Ren J, Su D, Bao M, Zhang M, Chen X, Li Y, Wang Z, Dai X, Wei Z, Zhang S, Zhang Y, Li J, Li X, Xu J, Mo N. Development and Validation of Machine Learning Models for Predicting Falls Among Hospitalized Older Adults: Retrospective Cross-Sectional Study. JMIR Aging 2026;9:e80602 View