Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation
Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation
复制标题
个性化和生成生理模型的集体变分推理：失血复苏案例研究
DOI：
10.1109/tbme.2021.3103141
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发表时间：
2022
影响因子：
4.6
通讯作者：
Hahn, Jin-Oh
中科院分区：
文献类型：
--
作者：
Tivay, Ali;Kramer, George C.;Hahn, Jin-Oh
个人生理实验通常提供有用的，但不完整的信息，研究的生理过程。因此，从实验数据推断生理模型的未知参数通常具有挑战性。本文的目的是提出和说明集体变分推理（C-VI）方法的有效性，旨在调和低信息和异构数据从一组实验，以产生强大的个性化和生成的生理models.MethodsTo获得C-VI方法，我们利用概率图形模型强加结构上的可用生理数据，以及使用变分贝叶斯推理技术在算法上表征图形模型。为了说明C-VI方法的有效性，我们将其应用于出血复苏的数学建模的案例研究。结果在出血复苏建模的背景下，C-VI方法可以协调多个实验中的血细胞比容、心输出量和血压数据的异质组合，以获得（i）稳健的个性化模型沿着相关的不确定性和信号质量测量，和（ii）一个生成模型能够再现的生理行为的population.ConclusionThe C-VI方法有利于个性化和生成建模的生理过程中存在的低信息和异构data.SignificanceThe产生的模型提供了坚实的基础，可解释的生理监测，决策支持和闭环控制算法的开发和测试。
ObjectiveIndividual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models.MethodsTo derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation.ResultsIn the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population.ConclusionThe C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data.SignificanceThe resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.