Decision support for personalized therapy in implantable medical devices: A digital twin approach

Abstract

This work presents a digital-twin-based decision-support framework for personalized therapy in implantable medical devices. It combines patient-specific physiological modeling, feature extraction, and reinforcement learning to update therapies over time, and a case study on implantable cardioverter defibrillators shows gains in both effectiveness and safety.