Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats.

ABSTRACT: Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure di

SUBMITTER: Longobardi S 

PROVIDER: S-EPMC7287330 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature