InVAErt networks for amortized inference and identifiability analysis of lumped parameter hemodynamic models
<|reference_start|>InVAErt networks for amortized inference and identifiability analysis of lumped parameter hemodynamic models: 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.<|reference_end|>
@article{tong2024invaert,
    title={InVAErt networks for amortized inference and identifiability analysis of
  lumped parameter hemodynamic models},
    author={Guoxiang Grayson Tong, Carlos A. Sing Long, Daniele E. Schiavazzi},
    journal={arXiv preprint arXiv:2408.08264},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08264},
    primaryClass={math.NA cs.AI cs.CE cs.LG cs.NA}
}