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

Digital Health Reviews (1759) Machine Learning (2134) Artificial Intelligence (2202) Myocardial Infarction (Heart Attack) (56) Stroke (252) Coronary Heart Disease (111) Applications of AI (301) Cardiac Risk and Cardiac Risk Calculators (110)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76215, first published .
Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

Authors of this article:

Min-Young Yu1 Author Orcid Image ;   Hae Young Yoo2 Author Orcid Image ;   Ga In Han1 Author Orcid Image ;   Eun-Jung Kim1 Author Orcid Image ;   Youn-Jung Son2 Author Orcid Image
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Journals

  1. Fan H, Fu X, Guo Q, Jia F, Wei X, Liu J, Zhang N, Zhu C, Shi J, Zhang L, Li J. Early diagnostic biomarkers for acute myocardial infarction unveiled by metabolomics, Mendelian randomization, and machine learning. Molecular Biomedicine 2026;7(1) View