Estimation of cardiovascular model parameters from electronic health records (EHR) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiability arises when a manifold in the space of parameters is mapped to a common output, while practical non-identifiability can result due to limited data, model misspecification, or noise corruption. To address the resulting ill-posed inverse problem, optimization-based or Bayesian inference approaches typically use regularization, thereby limiting the possibility of discovering multiple solutions. In this study, we use inVAErt networks, a neural network-based, data-driven framework for enhanced digital twin analysis of stiff dynamical systems. We demonstrate the flexibility and effectiveness of inVAErt networks in the context of physiological inversion of a six-compartment lumped parameter hemodynamic model from synthetic data to real data with missing components.

翻译：基于电子健康记录（EHR）估计心血管模型参数面临重大挑战，主要源于可辨识性的缺失。当参数空间中的一个流形映射到同一输出时，会产生结构上的不可辨识性；而数据有限、模型设定错误或噪声干扰则可能导致实际上的不可辨识性。为解决由此产生的病态反问题，基于优化或贝叶斯推断的方法通常采用正则化，从而限制了发现多重解的可能性。在本研究中，我们采用inVAErt网络——一种基于神经网络的、数据驱动的框架，用于增强刚性动力系统的数字孪生分析。我们展示了inVAErt网络在六室集总参数血流动力学模型生理反演中的灵活性与有效性，该反演过程从合成数据扩展到具有缺失分量的真实数据。