Considering discrepancy when calibrating a mechanistic electrophysiology model
This work tackles model uncertainty in cardiac electrophysiology, which is incremental as it builds on existing UQ methods by focusing on structural discrepancies.
The paper addresses uncertainty in cardiac simulation models by highlighting model discrepancy as an under-addressed source of uncertainty, providing examples at ion channel and action potential scales and attempting to account for it using Gaussian processes and ARMA models during calibration.
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 characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this 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 (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided.