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SHR Neuro Cancer Cardio Lipid Metab Microb

Rodero, C; Longobardi, S; Augustin, C; Strocchi, M; Plank, G; Lamata, P; Niederer, SA.
Calibration of Cohorts of Virtual Patient Heart Models Using Bayesian History Matching.
Ann Biomed Eng. 2023; 51(1):241-252 Doi: 10.1007/s10439-022-03095-9 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Co-authors Med Uni Graz
Augustin Christoph
Plank Gernot
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Abstract:
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.
Find related publications in this database (using NLM MeSH Indexing)
Humans - administration & dosage
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Calibration - administration & dosage
Heart - administration & dosage
Uncertainty - administration & dosage
Models, Statistical - administration & dosage

Find related publications in this database (Keywords)
Gaussian process emulator
Heart model
Statistical shape model
Uncertainty quantification
In-silico trial
Virtual clinical trial
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