Precision Allocation of GLP1-RA and SGLT2i in Cardiac Rehabilitation

Overview

Every year nearly half of cardiac rehab patients quit before finishing recovery. With a specific cardiac rehab program helping over 550 patients with heart disease and diabetes restrengthen cardiovascular health, there is need of a system to prevent data entry errors. I built a computer program that checks medication records at entry to program, 12 week, and dropout. The program finds medication information from records and associates the data with a quality grade, so care providers know which information is trustworthy. Now researchers can use accurate data to prove that rehab saves lives. To solve the initial patient dropout problem, I created a system that reads three sources of patient data (wearable, ECG, medical history) and predicts which patients are most likely to drop out, flagging them before its too late. This system correctly identifies high risk patients and could prevent thousands of dropouts annually across Canada, saving lives everywhere.

Awards (1)

Selected for CWSF 2026

Competition history

CWSF 2026 Disease & Illness

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