Calibration of Cohorts of Virtual Patient Heart Models Using Bayesian History Matching.
Gaussian process emulator
Heart model
In-silico trial
Statistical shape model
Uncertainty quantification
Virtual clinical trial
Journal
Annals of biomedical engineering
ISSN: 1573-9686
Titre abrégé: Ann Biomed Eng
Pays: United States
ID NLM: 0361512
Informations de publication
Date de publication:
Jan 2023
Jan 2023
Historique:
received:
23
05
2022
accepted:
29
09
2022
pubmed:
23
10
2022
medline:
13
1
2023
entrez:
22
10
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36271218
doi: 10.1007/s10439-022-03095-9
pii: 10.1007/s10439-022-03095-9
pmc: PMC9832095
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
241-252Subventions
Organisme : Wellcome Trust
ID : 209450/Z/17/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 203148/Z/16/Z
Pays : United Kingdom
Organisme : British Heart Foundation
ID : TG/17/3/33406
Pays : United Kingdom
Organisme : British Heart Foundation
ID : PG/16/75/32383
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RE/18/2/34213
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/20/4/34803
Pays : United Kingdom
Organisme : NIH HHS
ID : R01-HL152256
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL152256
Pays : United States
Informations de copyright
© 2022. The Author(s).
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