Lei, C.L., Ghosh, S., Whittaker, D.G. et al. (14 more authors) (2020) Considering discrepancy when calibrating a mechanistic electrophysiology model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 378 (2173). 20190349. ISSN 1364-503X
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’.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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. |
Keywords: | uncertainty quantification; cardiac model; Bayesian inference; model discrepancy |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/P010741/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/R006768/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/R003645/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Jun 2020 15:23 |
Last Modified: | 12 Jun 2020 15:23 |
Status: | Published |
Publisher: | The Royal Society |
Refereed: | Yes |
Identification Number: | 10.1098/rsta.2019.0349 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161513 |