Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid‐dynamics model of the pulmonary circulation,International Journal for Numerical Methods in Biomedical Engineering

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Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid‐dynamics model of the pulmonary circulation
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2020-11-28 , DOI: 10.1002/cnm.3421
L Mihaela Paun ₁ , Dirk Husmeier ₁

Affiliation

The past few decades have witnessed an explosive synergy between physics and the life sciences. In particular, physical modelling in medicine and physiology is a topical research area. The present work focuses on parameter inference and uncertainty quantification in a 1D fluid‐dynamics model for quantitative physiology: the pulmonary blood circulation. The practical challenge is the estimation of the patient‐specific biophysical model parameters, which cannot be measured directly. In principle this can be achieved based on a comparison between measured and predicted data. However, predicting data requires solving a system of partial differential equations (PDEs), which usually have no closed‐form solution, and repeated numerical integrations as part of an adaptive estimation procedure are computationally expensive. In the present article, we demonstrate how fast parameter estimation combined with sound uncertainty quantification can be achieved by a combination of statistical emulation and Markov chain Monte Carlo (MCMC) sampling. We compare a range of state‐of‐the‐art MCMC algorithms and emulation strategies, and assess their performance in terms of their accuracy and computational efficiency. The long‐term goal is to develop a method for reliable disease prognostication in real time, and our work is an important step towards an automatic clinical decision support system.

中文翻译：

马尔可夫链蒙特卡罗与高斯过程，用于肺循环一维流体动力学模型中的快速参数估计和不确定性量化

过去几十年见证了物理学和生命科学之间爆炸性的协同作用。特别是，医学和生理学中的物理建模是一个热门研究领域。目前的工作重点是定量生理学一维流体动力学模型中的参数推断和不确定性量化：肺血液循环。实际的挑战是估计患者特定的生物物理模型参数，这些参数无法直接测量。原则上，这可以基于测量数据和预测数据之间的比较来实现。然而，预测数据需要求解偏微分方程组（PDE），该系统通常没有封闭式解，并且作为自适应估计过程的一部分的重复数值积分在计算上是昂贵的。在本文中，我们演示了如何通过统计仿真和马尔可夫链蒙特卡罗 (MCMC) 采样的组合来实现快速参数估计和可靠的不确定性量化。我们比较了一系列最先进的 MCMC 算法和仿真策略，并评估它们的准确性和计算效率。长期目标是开发一种实时可靠的疾病预测方法，我们的工作是迈向自动临床决策支持系统的重要一步。

更新日期：2020-11-28

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