Imperial College London
Alternate

Dr Chris Cantwell

Faculty of Engineering, Department of Aeronautics

Senior Lecturer in Aeronautics
 
 
 
//

Contact

 

+44 (0)20 7594 5050c.cantwell Website

 
 
//

Location

 

Department of Aeronautics, Room 219City and Guilds BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Lei:2020:10.1098/rsta.2019.0349,
author = {Lei, CL and Ghosh, S and Whittaker, DG and Aboelkassem, Y and Beattie, KA and Cantwell, CD and Delhaas, T and Houston, C and Novaes, GM and Panfilov, AV and Pathmanathan, P and Riabiz, M and Dos, Santos RW and Walmsley, J and Worden, K and Mirams, GR and Wilkinson, RD},
doi = {10.1098/rsta.2019.0349},
journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
pages = {1--23},
title = {Considering discrepancy when calibrating a mechanistic electrophysiology model.},
url = {http://dx.doi.org/10.1098/rsta.2019.0349},
volume = {378},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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'.
AU  - Lei,CL
AU  - Ghosh,S
AU  - Whittaker,DG
AU  - Aboelkassem,Y
AU  - Beattie,KA
AU  - Cantwell,CD
AU  - Delhaas,T
AU  - Houston,C
AU  - Novaes,GM
AU  - Panfilov,AV
AU  - Pathmanathan,P
AU  - Riabiz,M
AU  - Dos,Santos RW
AU  - Walmsley,J
AU  - Worden,K
AU  - Mirams,GR
AU  - Wilkinson,RD
DO  - 10.1098/rsta.2019.0349
EP  - 23
PY  - 2020///
SN  - 1364-503X
SP  - 1
TI  - Considering discrepancy when calibrating a mechanistic electrophysiology model.
T2  - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
UR  - http://dx.doi.org/10.1098/rsta.2019.0349
UR  - https://www.ncbi.nlm.nih.gov/pubmed/32448065
UR  - https://royalsocietypublishing.org/doi/10.1098/rsta.2019.0349
UR  - http://hdl.handle.net/10044/1/79530
VL  - 378
ER  -
Download