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

Geroinformatics and Electronic Clinical Information/Decision Making in Geriatrics (37) Clinical Information and Decision Making (1915) Machine Learning (2076) Artificial Intelligence (2129) Cardiology (224) Cardiac Risk and Cardiac Risk Calculators (102) Decision Support for Health Professionals (1539)

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

Authors of this article:

Chang Liu1, 2, 3 Author Orcid Image ;   Kai Zhang1, 2, 3 Author Orcid Image ;   Xiaodong Yang4 Author Orcid Image ;   Bingbing Meng1, 2, 3 Author Orcid Image ;   Jingsheng Lou1, 2, 3 Author Orcid Image ;   Yanhong Liu1, 2, 3 Author Orcid Image ;   Jiangbei Cao1, 2, 3 Author Orcid Image ;   Kexuan Liu5 Author Orcid Image ;   Weidong Mi1, 2, 3 Author Orcid Image ;   Hao Li1, 2, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

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