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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

For example, Sim and Jeong [42] reported that the risk factors for AMI in Korean patients differ from those in Western populations. This geographic concentration introduces potential biases, reducing the applicability of findings to Western or ethnically diverse populations. In addition, the lack of prospective cohort studies limits our ability to evaluate the real-time clinical utility and temporal robustness of ML-based predictions.

Min-Young Yu, Hae Young Yoo, Ga In Han, Eun-Jung Kim, Youn-Jung Son

J Med Internet Res 2025;27:e76215