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Bayesian sensitivity analysis of a 1D vascular model with Gaussian process emulators
Author(s) -
Melis Alessandro,
Clayton Richard H.,
Marzo Alberto
Publication year - 2017
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.2882
Subject(s) - sensitivity (control systems) , monte carlo method , computer science , scalability , gaussian process , uncertainty quantification , uncertainty analysis , gaussian , mathematics , simulation , statistics , machine learning , engineering , physics , electronic engineering , quantum mechanics , database
One‐dimensional models of the cardiovascular system can capture the physics of pulse waves but involve many parameters. Since these may vary among individuals, patient‐specific models are difficult to construct. Sensitivity analysis can be used to rank model parameters by their effect on outputs and to quantify how uncertainty in parameters influences output uncertainty. This type of analysis is often conducted with a Monte Carlo method, where large numbers of model runs are used to assess input‐output relations. The aim of this study was to demonstrate the computational efficiency of variance‐based sensitivity analysis of 1D vascular models using Gaussian process emulators, compared to a standard Monte Carlo approach. The methodology was tested on four vascular networks of increasing complexity to analyse its scalability. The computational time needed to perform the sensitivity analysis with an emulator was reduced by the 99.96% compared to a Monte Carlo approach. Despite the reduced computational time, sensitivity indices obtained using the two approaches were comparable. The scalability study showed that the number of mechanistic simulations needed to train a Gaussian process for sensitivity analysis was of the order O ( d ) , rather than O ( d × 1 0 3 ) needed for Monte Carlo analysis (where d is the number of parameters in the model). The efficiency of this approach, combined with capacity to estimate the impact of uncertain parameters on model outputs, will enable development of patient‐specific models of the vascular system, and has the potential to produce results with clinical relevance.