InVAErt网络用于一次性参数血液动力学模型的摊销推断和识别分析
Guoxiang Grayson Tong1, Carlos A Sing-Long2, Daniele E Schiavazzi1
1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame College of Science, Notre Dame, IN 46556, USA.
概括
这项研究引入了inVAErt网络,从电子健康记录 (EHR) 中估计心血管模型参数. 这种数据驱动的方法克服了识别能力的挑战,即使使用不完整的数据,也可以实现准确的生理逆转.
科学领域:
- 计算生物学是一种计算生物学.
- 生物医学工程 生物医学工程
- 系统生理学 系统生理学
背景情况:
- 从电子健康记录 (EHR) 中估计心血管模型参数是具有挑战性的,因为识别问题.
- 不能识别,无论是结构性的还是实际的,都使生理学建模中的反向问题复杂化.
- 当前的方法经常使用规范化,这可能会限制发现多个有效解决方案.
研究的目的:
- 引入和评估inVAErt网络,一个用于数字双胞胎分析的新型神经网络框架.
- 在心血管模型中解决参数估计的反向问题.
- 通过使用各种数据集来证明inVAErt网络在生理逆转中的能力.
主要方法:
- 在VAErt网络中使用,数据驱动的神经网络框架.
- 将框架应用于一个六个隔间一次性参数的血液动力学模型.
- 在合成和现实世界的电子健康记录数据上测试了该方法,包括缺失组件的场景.
主要成果:
- inVAErt网络有效地处理了心血管模型参数估计中的识别性挑战.
- 该框架在生理逆转方面表现出灵活性和有效性.
- 对缺少组件的真实数据的成功应用突出了实际实用性.
结论:
- inVAErt网络提供了一种强大的数据驱动方法,用于增强动态系统的数字双胞胎分析.
- 该方法为电子健康记录的参数估计提供了强大的解决方案,克服了识别能力的局限性.
- 这一框架有望推进医疗保健和生物系统中的心血管建模和生理逆转.
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