Key result
Bayesian history matching finds ~87% of non-implausible parameters without extra simulations, accelerating heart model calibration.
Why the study?
Previous patient-specific model calibration techniques treated each patient independently, making them computationally expensive for large-scale clinical adoption.
Bayesian History Matching using Gaussian Process Emulators significantly accelerates the calibration of virtual patient heart models, reducing the computational load for future biomedical studies.
Answers are grounded in this paper and cite their sources.
Reduces computational burden in virtual heart modeling; leaves open whether calibrated models improve clinical predictions.
Previous patient-specific model calibration techniques have treated each patient independently, making the methods expensive for large-scale clinical adoption. In this work, we show how we can reuse simulations to accelerate the patient-specific model calibration pipeline. To represent anatomy, we used a Statistical Shape Model and to represent function, we ran electrophysiological simulations. We study the use of 14 biomarkers to calibrate the model, training one Gaussian Process Emulator (GPE) per biomarker. To fit the models, we followed a Bayesian History Matching (BHM) strategy, wherein each iteration a region of the parameter space is ruled out if the emulation with that set of parameter values produces is "implausible". We found that without running any extra simulations we can find 87.41% of the non-implausible parameter combinations. Moreover, we showed how reducing the uncertainty of the measurements from 10 to 5% can reduce the final parameter space by 6 orders of magnitude. This innovation allows for a model fitting technique, therefore reducing the computational load of future biomedical studies.
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Rodero et al. (2022) studied Healthy human heart electrophysiology models (n=19). Bayesian History Matching (BHM) with Gaussian Process Emulators (GPE) vs. Classical patient-specific model calibration was evaluated on Identification of non-implausible parameter combinations without extra simulations. Using Bayesian History Matching and Gaussian Process Emulators, 87.41% of non-implausible parameter combinations can be found without running extra simulations, accelerating heart model calibration.
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