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Considering discrepancy when calibrating a mechanistic electrophysiology model.

Chon Lok Lei1, Sanmitra Ghosh2, Dominic G Whittaker3

  • 1Computational Biology and Health Informatics, Department of Computer Science, University of Oxford, Oxford, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 26, 2020
PubMed
Summary

This study addresses model discrepancy, an under-addressed uncertainty in cardiac simulations. Accounting for this uncertainty in model structure improves predictions and model calibration, crucial for reliable decision-making.

Keywords:
Bayesian inferencecardiac modelmodel discrepancyuncertainty quantification

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Area of Science:

  • Computational Biology
  • Biophysics
  • Mathematical Modeling

Background:

  • Uncertainty quantification (UQ) is essential for decision-making using mathematical models.
  • Cardiac simulation increasingly uses UQ for input and output uncertainty.
  • Model structure uncertainty, or model discrepancy, is an under-addressed UQ aspect.

Purpose of the Study:

  • To highlight model discrepancy as a critical uncertainty source in cardiac modeling.
  • To demonstrate the impact of model discrepancy on model calibration and validation.
  • To explore methods for quantifying and accounting for model discrepancy.

Main Methods:

  • Reviewed UQ in cardiac simulations, focusing on model discrepancy.
  • Provided examples of discrepancy consequences at ion channel and action potential scales.
  • Applied Gaussian processes and autoregressive-moving-average models to quantify discrepancy during model calibration.

Main Results:

  • Model discrepancy significantly impacts model calibration and predictions.
  • Gaussian processes and autoregressive-moving-average models offer different advantages and limitations for discrepancy modeling.
  • Specific methods were evaluated for their effectiveness in accounting for model discrepancy.

Conclusions:

  • Addressing model discrepancy is vital for accurate and reliable cardiac simulations.
  • Further research is needed to develop and refine methods for quantifying model structure uncertainty.
  • Improved UQ, including model discrepancy, will enhance the utility of cardiac models in clinical decision-making.