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

Chung, Cheuk To, Bazoukis, George, Lee, Sharen, Liu, Ying, Liu, Tong, Letsas, Konstantinos P., Armoundas, Antonis A., Tse, Gary (2022) Machine learning techniques for arrhythmic risk stratification: a review of the literature. International Journal of Arrhythmia, (23). Article Number 10 (2022). ISSN 2466-1171. (doi:10.1186/s42444-022-00062-2) (KAR id:98716)

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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.

Item Type: Article
DOI/Identification number: 10.1186/s42444-022-00062-2
Uncontrolled keywords: Artificial intelligence; Machine learning, Ventricular arrhythmias; Ventricular tachycardia; Ventricular fibrillation; Risk stratification; Prediction models
Subjects: R Medicine > R Medicine (General)
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
Depositing User: Manfred Gschwandtner
Date Deposited: 05 Dec 2022 17:53 UTC
Last Modified: 13 Dec 2022 11:18 UTC
Resource URI: (The current URI for this page, for reference purposes)
Tse, Gary:
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