ArticleProceedings of the National Academy of Sciences of the United States of America2021
Prediction of arrhythmia susceptibility through mathematical modeling and machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 38 citations in OpenAlex.
- Integrating AI-Driven Diagnostics in Arrhythmia Care to Enhance Patient Outcomes: A Narrative Review.Cureus · 2026Review
- Population Modeling Approach for Human Cardiac Arrhythmia Risk Prediction.Circulation. Arrhythmia and electrophysiology · 2026Article
- Interplay between variability in intrinsic cellular properties and heart failure-associated remodeling in a simulated population with human heart failure.Journal of molecular and cellular cardiology · 2026Article
- Quantitative assessment of the usability of electromechanical human-based modelling and simulation to replace Langendorff isolated rabbit heart experiments in the preclinical setting.Frontiers in pharmacology · 2025Article
- Article
- The Current State of Realistic Heart Models for Disease Modelling and Cardiotoxicity.International journal of molecular sciences · 2024Review
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Arrhythmogenic Manifestations of Chagas Disease: Perspectives From the Bench to Bedside.Circulation research · 2024Review
- Pole balancing on the fingertip: model-motivated machine learning forecasting of falls.Frontiers in physiology · 2024Article
- Assessing drug safety by identifying the axis of arrhythmia in cardiomyocyte electrophysiology.eLife · 2023Article
- Estimating the probability of early afterdepolarizations and predicting arrhythmic risk associated with long QT syndrome type 1 mutations.Biophysical journal · 2023Article
- Animal Disease Models and Patient-iPS-Cell-Derived In Vitro Disease Models for Cardiovascular Biology-How Close to Disease?Biology · 2023Review
- Modeling incomplete penetrance in long QT syndrome type 3 through ion channel heterogeneity: an in silico population study.American journal of physiology. Heart and circulatory physiology · 2023Article
- Quantitative approaches to drug safety: The 2022 PSP special issue.CPT: pharmacometrics & systems pharmacology · 2022Article
- Benefits of SGLT2 inhibitors in arrhythmias.Frontiers in cardiovascular medicine · 2022Review
- Cross clinical-experimental-computational qualification ofFrontiers in toxicology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
At present, the QT interval on the electrocardiographic (ECG) waveform is the most common metric for assessing an individual's susceptibility to ventricular arrhythmias, with a long QT, or, at the cellular level, a long action potential duration (APD) considered high risk. However, the limitations of this simple approach have long been recognized. Here, we sought to improve prediction of arrhythmia susceptibility by combining mechanistic mathematical modeling with machine learning (ML). Simulations with a model of the ventricular myocyte were performed to develop a large heterogenous population of cardiomyocytes (
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