Risk Stratification for Patients with Cardiovascular Disease

We are interested in developing automated methods that can identify patients with cardiovascular disease who are at high risk of adverse outcomes.  To do this we employ a variety of different methods grounded in signal progressing and machine learning. Our methods combine disparate types of clinical information (e.g., medical history, genetic information, physiologic signals) to arrive at models that can guide clinical decision making.