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

Abstract

Implantable Medical Devices (IMDs) offer timely therapeutic interventions for life-threatening conditions without disrupting patients’ daily activities. Given the substantial variability in individual health conditions, customizing device therapies to suit specific patients and their evolving needs is crucial. Although current customization relies on modifying device parameter settings, no universally accepted clinical guidelines exist for this form of customization, particularly for patients with complex or rare conditions. To address this gap, we introduce a decision support framework that leverages expert system methodologies for personalized therapy in IMDs. This comprehensive expert system encompasses a dynamic digital twin of the patient, built using physiological data obtained from the IMD and additional wearable devices. The digital twin captures disease mechanisms and patient-specific physiological parameters, while a periodically trained Reinforcement Learning (RL) agent and specialized algorithms for the ongoing maintenance of the digital twin complete the expert system. Together, these elements facilitate periodic, informed therapy customization based on up-to-date patient conditions. Our approach includes novel algorithms for feature extraction, physiological modeling, and the maintenance of the digital twin. We substantiate the efficacy and safety of our digital-twin-based decision support framework through a case study focused on Implantable Cardioverter Defibrillators (ICDs). Experimental results on virtual patients confirm that our integrated framework surpasses traditional customization strategies in both therapeutic effectiveness and patient safety considerations.