HyPer-EP：用于心脏电生理学的元学习混合个性化模型
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology
AI总结：
提出一种新颖的混合建模与元学习框架，将基于物理的已知表达式与神经网络结合，实现个性化心脏数字孪生模型中物理与神经组件的分别识别，并在合成实验中验证了其可行性与通用性。
摘要：
个性化虚拟心脏模型在临床应用中展现出越来越大的潜力，尽管在给定患者特异性数据的情况下，其参数估计仍然是一个挑战。传统的基于物理的建模方法计算成本高昂，且往往由于模型简化和假设而忽略了这些模型中固有的结构误差。另一方面，现代深度学习方法严重依赖数据监督，并且缺乏可解释性。在本文中，我们提出了一种新颖的混合建模框架，将个性化心脏数字孪生描述为基于物理的已知表达式与对其未知现实差距的神经网络建模的结合。然后，我们提出了一种新颖的元学习框架，以实现对混合模型中基于物理的组件和神经组件的分别识别。我们通过两个实例化示例及其在合成实验中的概念验证，证明了这种混合建模框架的可行性和通用性。
英文摘要：
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 personalized cardiac digital twin as a combination of a physics-based known expression augmented by neural network modeling of its unknown gap to reality. We then present a novel meta-learning framework to enable the separate identification of both the physics-based and neural components in the hybrid model. We demonstrate the feasibility and generality of this hybrid modeling framework with two examples of instantiations and their proof-of-concept in synthetic experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
