Computational Biology and Health Informatics, Department of Computer Science, University of Oxford, Oxford, UK.
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Philos Trans A Math Phys Eng Sci. 2020 Jun 12;378(2173):20190349. doi: 10.1098/rsta.2019.0349. Epub 2020 May 25.
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 , 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'.
不确定性量化 (UQ) 是使用数学模型和模拟做出决策的重要步骤。心脏模拟领域已经开始探索和采用 UQ 方法来描述模型输入中的不确定性以及这种不确定性如何传播到输出或预测中；本期刊物中的论文就是这方面的例子。在这篇综述和观点文章中，我们提请注意我们预测中一个重要但未得到充分重视的不确定性来源——模型结构或方程本身的不确定性。模型的不完善与现实之间的差异称为, 我们通常不确定这种差异的大小和后果。在这里，我们提供了在离子通道和动作电位尺度上对模型进行校准时差异后果的两个例子。此外，我们尝试使用基于高斯过程和自回归移动平均模型对差异进行建模的不同方法来校准和验证离子通道模型时考虑到这种差异，然后突出每种方法的优缺点。最后，为未来的工作提供了建议和研究方向。本文是主题为“心脏和心血管建模与模拟中的不确定性量化”特刊的一部分。
