Phenotyping of AMI patients using machine learning: integration of clinical, metabolic, and behavioral features to identify high-risk "frailty-inflammation" phenotype

Background

Prognosis among patients with acute myocardial infarction (AMI) shows substantial heterogeneity, making precise identification of high-risk phenotypes essential for improving long-term outcomes. Machine learning techniques were used to identify such phenotypes by integrating multidimensional data and to examine their association with major adverse cardiovascular events (MACE).