HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology

arXiv:2403.15433v1 Announce Type: cross
Abstract: Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challenge. Traditional physics-based modeling approaches are computationally costly and often neglect the inherent structural errors in these models due to model simplifications and assumptions. Modern deep learning approaches, on the other hand, rely heavily on data supervision and lacks interpretability. In this paper, we present a novel hybrid modeling framework to describe a …

abstract arxiv assumptions challenge clinical cs.ai cs.lg data eess.iv eess.sp errors hybrid meta meta-learning modeling modern parameters patient personalized physics type virtual