从EHR数据中确定ICU患者的表型的方法:高准确性,个性化和可解释的表型估计
medRxiv : the preprint server for health sciences
|September 4, 2023
概括
这项研究引入了一种新的方法,可以从电子健康记录 (EHR) 中创建个性化的实时生理现象型. 这些可解释的表型通过估计像胰岛素分泌等未测量的生物标志物来改善患者护理.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生理学计算生理学
- 系统生物学 系统生物学
背景情况:
- 从复杂的生理和医疗数据中计算高准确度,时间依赖的表型是具有挑战性的.
- 电子健康记录 (EHR) 包含丰富的数据,但需要先进的方法来提取个性化的生理洞察力.
- 当前的表型化方法往往缺乏个性化医学所需的时间分辨率和机械解释性.
研究的目的:
- 从电子健康记录数据中估计未测量的生理参数的方法管道开发.
- 为了产生高保真性,个性化的表型,定于生理力学机制.
- 为临床应用创建可解释的,时间依赖的计算生物标志物.
主要方法:
- 在ICU患者的葡萄糖-胰岛素系统中使用反向问题框架和数据同化开发了表型管道.
- 应用随机优化来估计数学生理模型,推导胰岛素分泌,清除和抵抗的参数.
- 在估计的生理参数上利用无监督的机器学习来生成离散的表型标签.
主要成果:
- 计算了109名ICU患者的连续和离散生理表型,反映了三天窗口中未测量的胰岛素动态.
- 在连续的三天时间内识别了六,六和五种不同的表型,表型标签达到89%的预测准确度.
- 外部验证证明了与实验室测量和临床代码的一致性,以及胰岛素分泌的高一致性 (83%±27%).
结论:
- 开发的生理现象类型具有高度准确性,连续性,时间特异性,个性化,可解释性和预测性.
- 这种方法提供了一种可通用的方法,以发现更深层次的生理信息,以提供个性化的医疗护理.
- 该管道允许从例行收集的EHR数据中创建新的,临床相关的表型.
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