HAPI-EP: Towards Hybrid, Adaptive, and Predictive Digital Twins of Cardiac Electrophysiology
Researchers have developed HAPI-EP, an AI framework designed to create hybrid, adaptive, and predictive digital twins for cardiac electrophysiology. This framework integrates mechanistic and data-driven models, allowing for rapid on-the-fly adaptation to live patient data. By using feedforward meta-learners and predictive objectives, HAPI-EP aims to achieve theoretical identifiability and strong predictive capabilities, even in out-of-distribution scenarios. AI
IMPACT This framework could advance personalized medicine by enabling more accurate and adaptive digital twins for cardiac conditions.