Virtual Patient Generation using Physiological Models through a Compressed Latent Parameterization
Virtual Patient Generation using Physiological Models through a Compressed Latent Parameterization
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DOI：
10.23919/acc45564.2020.9147298
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
A. Tivay;G. Kramer;J. Hahn
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文献类型：
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作者：
A. Tivay;G. Kramer;J. Hahn
本文提出了一种数据驱动的方法，利用生理过程的数学模型来生成虚拟患者。此类模型通常包含大量可调参数，必须对这些参数进行校准，以捕获数据集中每个真实患者的观察特征。通过从这个参数空间中采样，可以生成潜在的新的虚拟患者。然而，通常情况下，虚拟患者的结果集包含表现出生理上不现实行为的成员。在目前的工作中，我们采用了一个实际重要的案例研究，对出血和液体复苏的心血管反应进行建模，以证明在数据集中观察到的受试者特定特征可以在高度压缩的潜在参数空间中替代表示，而不会对每个真实患者的校准误差造成重大损失。然后，我们表明，通过从这个潜在参数空间采样，有可能产生新的虚拟患者，也表现出生理上真实的行为。
This paper presents a data-driven approach to generating virtual patients using mathematical models of physiological processes. Such models often contain a large number of tunable parameters that must be calibrated to capture the observed characteristics of each real patient in a dataset. By sampling from this parameter space, potentially new virtual patients can be generated. However, it is often the case that the resulting set of virtual patients contains members that exhibit physiologically unrealistic behavior. In the present work, we employ a practically important case study on the modeling of cardiovascular responses to hemorrhage and fluid resuscitation in order to demonstrate that subject-specific characteristics observed in a dataset can be alternatively represented within a highly compressed latent parameter space without significant losses in calibration error for each real patient. Then, we show that by sampling from this latent parameter space, it is possible to generate new virtual patients that also exhibit physiologically realistic behavior.