Int J Arrhythm.  2022 Jun;23(2):10. 10.1186/s42444-022-00062-2.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Affiliations
  • 1Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong, China
  • 2Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus
  • 3Medical School, University of Nicosia, Nicosia, Cyprus
  • 4Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
  • 5Tianjin Key Laboratory of Ionic‑Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300211, China
  • 6Department of Electrophysiology, Onassis Cardiac Surgery Center, Athens, Greece
  • 7Cardiovascular Research Center, Massachusetts General Hospital, 149 13th Street, Charlestown, Boston, MA 02129, USA
  • 8Institute for Medical Engineering and Science, Massachusetts Institute of Technology Cambridge, Cambridge, MA, USA
  • 9Kent and Medway Medical School, Canterbury, Kent, UK.

Abstract

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice

Keyword

Artificial intelligence; Machine learning; Ventricular arrhythmias; Ventricular tachycardia; Ventricular fibrillation; Risk stratification; Prediction models
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