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Machine learning techniques for arrhythmic risk stratification: a review of the literature
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 stratificat...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020640/ https://www.ncbi.nlm.nih.gov/pubmed/35449883 http://dx.doi.org/10.1186/s42444-022-00062-2 |
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author | Chung, Cheuk To Bazoukis, George Lee, Sharen Liu, Ying Liu, Tong Letsas, Konstantinos P. Armoundas, Antonis A. Tse, Gary |
author_facet | Chung, Cheuk To Bazoukis, George Lee, Sharen Liu, Ying Liu, Tong Letsas, Konstantinos P. Armoundas, Antonis A. Tse, Gary |
author_sort | Chung, Cheuk To |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9020640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-90206402022-04-20 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 Int J Arrhythmia Article 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. 2022 2022-04-01 /pmc/articles/PMC9020640/ /pubmed/35449883 http://dx.doi.org/10.1186/s42444-022-00062-2 Text en https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chung, Cheuk To Bazoukis, George Lee, Sharen Liu, Ying Liu, Tong Letsas, Konstantinos P. Armoundas, Antonis A. Tse, Gary Machine learning techniques for arrhythmic risk stratification: a review of the literature |
title | Machine learning techniques for arrhythmic risk stratification: a review of the literature |
title_full | Machine learning techniques for arrhythmic risk stratification: a review of the literature |
title_fullStr | Machine learning techniques for arrhythmic risk stratification: a review of the literature |
title_full_unstemmed | Machine learning techniques for arrhythmic risk stratification: a review of the literature |
title_short | Machine learning techniques for arrhythmic risk stratification: a review of the literature |
title_sort | machine learning techniques for arrhythmic risk stratification: a review of the literature |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020640/ https://www.ncbi.nlm.nih.gov/pubmed/35449883 http://dx.doi.org/10.1186/s42444-022-00062-2 |
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