血液和呼吸酒精度从皮肤酒精生物传感器数据:估计和不确定性量化通过前向和反向选为共变依赖的,物理信息,隐藏的马尔科夫模型
Clemens Oszkinat1, Tianlan Shao1, Chunming Wang1
1Department of Mathematics, University of Southern California, Los Angeles, CA 90089, USA.
Inverse problems
|September 20, 2023
概括
透皮酒精生物传感器提供持续监测,改善酒精研究. 一个新的基于物理学的模型,从通过皮肤的酒精度 (TAC) 数据准确地估计了呼吸酒精度 (BrAC).
科学领域:
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
- 药理动力学 药理动力学
背景情况:
- 目前的酒精监测依赖于气息测试仪和日记,限制了持续的数据收集.
- 精确的转换通过皮肤的酒精度 (TAC) 血/呼吸酒精度 (BAC/BrAC) 对于实际的生物传感器应用至关重要.
研究的目的:
- 使用先进的建模技术,开发一种新的方法,从通过皮肤的酒精度 (TAC) 来估计呼吸中酒精度 (BrAC).
- 将乙醇运输的物理原理与数据驱动学习相结合,以提高准确性.
主要方法:
- 开发了一个共变量依赖的,基于物理知识的隐藏马尔科夫模型,具有两种排放.
- 该模型将酒精动态的隐藏马尔科夫链与BRAC和TAC的双变量正常分布相结合,并结合了共变量.
- 一种混合方法将隐藏的马尔科夫模型规范化为第一原理部分微分方程 (PDE) 模型,使用姆-韦尔奇算法进行训练.
主要成果:
- 基于物理的维特比算法被用于向前过TAC以估计BRAC.
- 该模型在估计和实际BrAC之间达到了很好的一致性,测试组的中位数相对峰值误差为22%,曲线误差下的中位数相对面积为25%.
- 在BRAC估计中的非物理文物被基于物理的维特比算法消除了.
结论:
- 开发的基于物理知识的隐藏马尔科夫模型提供了一个准确可靠的方法来估计TAC的BRAC.
- 这种方法提高了通过皮肤的酒精生物传感器的实用性,用于在研究和临床环境中持续监测酒精.
- 混合建模策略有效地将物理理解与基于数据的学习相结合,以获得可靠的估计.
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