Global sensitivity analysis for assessing the parameters importance and setting a stopping criterion in a biomedical inverse problem,International Journal for Numerical Methods in Biomedical Engineering

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Global sensitivity analysis for assessing the parameters importance and setting a stopping criterion in a biomedical inverse problem
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2021-03-24 , DOI: 10.1002/cnm.3458
Robert Rapadamnaba ₁ , Mathieu Ribatet ₂ , Bijan Mohammadi ₁

Affiliation

This paper shows how to obtain in addition to the standard deviations available after a data assimilation procedure based on the ensemble Kalman filter, an apportioning of the total uncertainty in the outputs of a patient-specific blood flow model into small portions of uncertainty due to input parameters. Statistical indicators generally used for identifying the importance of numerical parameters, namely the Sobol' first order and total indices, are introduced and discussed. These allow the identification of the importance rank of the different input parameters for the patient-specific blood flow model, as well as the influence of the interactions between these parameters on the model output variance. The results show that knowing the importance rank of the model input parameters during the assimilation procedure is useful to avoid unnecessary over-solving and to find a suitable stopping criterion in clinical situations where faster diagnosis is always requested. Indeed, the work permits to reduce typically by a factor of six the time to solution and most importantly with very limited extra calculation using already available information.

中文翻译：

用于评估参数重要性和设置生物医学逆问题中的停止标准的全局敏感性分析

本文展示了如何在基于集成卡尔曼滤波器的数据同化程序之后获得可用的标准偏差，将患者特定血流模型输出中的总不确定性分配为由于输入引起的小部分不确定性参数。介绍和讨论了通常用于确定数值参数重要性的统计指标，即 Sobol 一阶指数和总指数。这些允许识别患者特定血流模型的不同输入参数的重要性等级，以及这些参数之间的相互作用对模型输出方差的影响。结果表明，在同化过程中了解模型输入参数的重要性等级有助于避免不必要的过度求解，并在总是需要更快诊断的临床情况下找到合适的停止标准。事实上，这项工作通常可以将求解时间减少六倍，最重要的是，使用现有信息进行非常有限的额外计算。

更新日期：2021-03-24

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