Daotong Guo

Abstract Myocardial infarction (MI) is often complicated by heterogeneous life-threatening conditions, which require outcome-specific risk stratification. Current models typically predict a single composite endpoint and fail to fully exploit inter-patient similarities and temporal dynamics in electronic health records. To address this crucial gap, we present the first graph neural network framework that simultaneously predicts 12 distinct post-MI complications and in-hospital mortality.