TY - JOUR
T1 - Considering discrepancy when calibrating a mechanistic electrophysiology model
AU - Lei, Chon Lok
AU - Ghosh, Sanmitra
AU - Whittaker, Dominic G.
AU - Aboelkassem, Yasser
AU - Beattie, Kylie A.
AU - Cantwell, Chris D.
AU - Delhaas, Tammo
AU - Houston, Charles
AU - Novaes, Gustavo Montes
AU - Panfilov, Alexander V.
AU - Pathmanathan, Pras
AU - Riabiz, Marina
AU - dos Santos, Rodrigo Weber
AU - Walmsley, John
AU - Worden, Keith
AU - Mirams, Gary R.
AU - Wilkinson, Richard D.
PY - 2020/6/12
Y1 - 2020/6/12
N2 - 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'.
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'.
KW - BAYESIAN CALIBRATION
KW - Bayesian inference
KW - FREQUENCY
KW - PREDICTION
KW - SYSTEMS
KW - VERIFICATION
KW - cardiac model
KW - model discrepancy
KW - uncertainty quantification
U2 - 10.1098/rsta.2019.0349
DO - 10.1098/rsta.2019.0349
M3 - (Systematic) Review article
C2 - 32448065
SN - 1364-503X
VL - 378
JO - Philosophical Transactions of the Royal Society A: mathematical Physical and Engineering Sciences
JF - Philosophical Transactions of the Royal Society A: mathematical Physical and Engineering Sciences
IS - 2173
M1 - 20190349
ER -