Prediction of arrhythmia susceptibility through mathematical modeling and machine learning
Meera Varshneya, Xueyan Mei, Eric A. Sobie
Abstract
Significance Despite our understanding of the many factors that promote ventricular arrhythmias, it remains difficult to predict which specific individuals within a population will be especially susceptible to these events. We present a computational framework that combines supervised machine learning algorithms with population-based cellular mathematical modeling. Using this approach, we identify electrophysiological signatures that classify how myocytes respond to three arrhythmic triggers. Our predictors significantly outperform the standard myocyte-level metrics, and we show that the approach provides insight into the complex mechanisms that differentiate susceptible from resistant cells. Overall, our pipeline improves on current methods and suggests a proof of concept at the cellular level that can be translated to the clinical level.