AI framework HAPI-EP enables adaptive cardiac digital twins

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.

RANK_REASON The cluster contains an academic paper detailing a new AI framework for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• HAPI-EP
• Hapithus
• Hugging Face
• ScienceCast
• Sumeet Vadhavkar

• paper
• model release

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