Global sensitivity analysis for assessing the parameters importance and setting a stopping criterion in a biomedical inverse problem

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.

Automated summary

This paper demonstrates how to partition the total uncertainty in the outputs of a patient-specific blood flow model into small portions of uncertainty due to input parameters after a data assimilation procedure based on ensemble Kalman filter. It introduces statistical indicators for identifying the importance of numerical parameters and shows the importance of knowing the rank of model input parameters during the assimilation procedure to avoid unnecessary over-solving and to find a suitable stopping criterion in clinical situations.