Practical Use of Regularization in Individualizing a Mathematical Model of Cardiovascular Hemodynamics Using Scarce Data
Practical Use of Regularization in Individualizing a Mathematical Model of Cardiovascular Hemodynamics Using Scarce Data
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DOI：
10.3389/fphys.2020.00452
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发表时间：
2020-05-26
影响因子：
4
通讯作者：
Hahn, Jin-Oh
中科院分区：
文献类型：
--
作者：
Tivay, Ali;Jin, Xin;Hahn, Jin-Oh
针对患者的个性化生理模型可以在重症监护环境中实现针对患者的监测和治疗。然而，由于模型复杂性和数据稀缺性之间的冲突，该任务往往提出了独特的“实际可识别性”挑战。正则化提供了一个已建立的框架，通过用先验知识补偿数据稀缺性来处理这种冲突。然而，在个体化生理模型中，规范化并没有得到广泛的应用，以促进患者特异性重症监护。因此，这项工作的目标是通过研究其在出血和循环复苏中血容量动力学和心血管血流动力学的临床重要重症病例研究中的作用，获得规范化在个性化复杂生理模型中使用稀缺数据的实际应用的潜在推广见解。我们构建了一个总体-平均模型作为先验知识，并通过正则化对生理模型进行个性化，以说明正则化可以有效地个性化生理模型，以学习显著的个体特异性特征（导致对个体特异性数据的拟合优度），同时限制与总体-平均模型的不必要偏差（实现实际可识别性）。我们还说明，正则化产生简约的个体化，只有敏感参数，以及足够的生理合理性和相关性，在预测内部生理状态。
Individualizing physiological models to a patient can enable patient-specific monitoring and treatment in critical care environments. However, this task often presents a unique "practical identifiability" challenge due to the conflict between model complexity and data scarcity. Regularization provides an established framework to cope with this conflict by compensating for data scarcity with prior knowledge. However, regularization has not been widely pursued in individualizing physiological models to facilitate patient-specific critical care. Thus, the goal of this work is to garner potentially generalizable insight into the practical use of regularization in individualizing a complex physiological model using scarce data by investigating its effect in a clinically significant critical care case study of blood volume kinetics and cardiovascular hemodynamics in hemorrhage and circulatory resuscitation. We construct a population-average model as prior knowledge and individualize the physiological modelviaregularization to illustrate that regularization can be effective in individualizing a physiological model to learn salient individual-specific characteristics (resulting in the goodness of fit to individual-specific data) while restricting unnecessary deviations from the population-average model (achieving practical identifiability). We also illustrate that regularization yields parsimonious individualization of only sensitive parameters as well as adequate physiological plausibility and relevance in predicting internal physiological states.