Considering discrepancy when calibrating a mechanistic electrophysiology model.
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-moving-average models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
Date Issued
2020-06-12
Date Acceptance
2020-05-01
Citation
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020, 378 (2173), pp.1-23
ISSN
1364-503X
Publisher
Royal Society, The
Start Page
1
End Page
23
Journal / Book Title
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume
378
Issue
2173
Copyright Statement
© 2020 The Authors. Published by the Royal Society under the terms of the
Creative Commons Attribution License http://creativecommons.org/licenses/
by/4.0/, which permits unrestricted use, provided the original author and
source are credited.
Creative Commons Attribution License http://creativecommons.org/licenses/
by/4.0/, which permits unrestricted use, provided the original author and
source are credited.
Sponsor
British Heart Foundation
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/32448065
Grant Number
PG/15/59/31621
Subjects
Bayesian inference
cardiac model
model discrepancy
uncertainty quantification
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2020-05-25