Accounting for model discrepancy using Gaussian processes and autoregressive-moving-average models highlights advantages and shortcomings in calibrating cardiac electrophysiology models.
Accounting for model discrepancy using methods like Gaussian processes is crucial for accurate calibration and validation of mechanistic cardiac electrophysiology models.
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’.
Lei et al. (Sun,) conducted a review in Cardiac electrophysiology modeling. Uncertainty quantification and model discrepancy calibration was evaluated. Accounting for model discrepancy using Gaussian processes and autoregressive-moving-average models highlights advantages and shortcomings in calibrating cardiac electrophysiology models.