AI-Enabled ECG Risk Stratification for Acute Coronary Syndrome

This project focuses on developing machine learning models to support early risk stratification of patients presenting with acute coronary syndrome using electrocardiographic features, vital signs, and demographic information available at the point of first medical contact. Using data from a large, diverse healthcare system, the model evaluates whether quantitative ECG features extracted from admission electrocardiograms can predict in-hospital mortality and identify patients at elevated risk for clinical deterioration. The project also uses model interpretability methods to assess the relative contribution of ECG-derived variables compared with conventional clinical features. More broadly, this work explores how routinely acquired ECG data can be leveraged as an AI-enabled prognostic tool to support timely triage, escalation of care, and clinical decision-making in acute cardiovascular settings.