Using Data-Assimilation to Predict Drug Perturbations
Loading...
Date
2025-05
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Ohio State University
Abstract
Cardiac arrhythmias, characterized by irregular heart rhythms, pose significant risks including stroke and cardiac arrest. Current methods to model cardiac electrophysiology often fail to capture individual variability and the full impact of drug perturbations. This study uses data-assimilation with the Tomek-Rudy ventricular myocyte model to improve the accuracy of reconstructing cardiac cell dynamics. Using the Ensemble Kalman Filter, simulations tested varying ensemble sizes, observation intervals, and inflation factors. Results showed increased ensemble size and reduced observation interval decreased reconstruction error, while high inflation factors improved accuracy but increased sensitivity to noise. This approach advances personalized medicine by offering a tailored method to predict cardiac responses to drugs, potentially enhancing treatment strategies and clinical outcomes.