Global sensitivity analysis for assessing the parameters importance and setting a stopping criterion in a biomedical inverse problem
| Autor: | Bijan Mohammadi, Robert Rapadamnaba, Mathieu Ribatet |
|---|---|
| Přispěvatelé: | Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Rank (linear algebra) Computer science uncertainty quantification 0206 medical engineering Biomedical Engineering 02 engineering and technology Sobol' sensitivity indices 030204 cardiovascular system & hematology Standard deviation 03 medical and health sciences 0302 clinical medicine Data assimilation sensitivity analysis convergence stopping criterion [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Humans Uncertainty quantification Molecular Biology Estimation theory Applied Mathematics hemodynamic inverse problems Hemodynamics Uncertainty Sobol sequence Variance (accounting) 020601 biomedical engineering Computational Theory and Mathematics Modeling and Simulation Ensemble Kalman filter parameter estimation Software |
| Zdroj: | International Journal for Numerical Methods in Biomedical Engineering International Journal for Numerical Methods in Biomedical Engineering, John Wiley and Sons, 2021 |
| ISSN: | 2040-7939 2040-7947 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text