Machine learning techniques for arrhythmic risk stratification: a review of the literature
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 u...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BioMed Central
2022
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| Online Access: | https://hdl.handle.net/1721.1/141638 |
| _version_ | 1811093596211773440 |
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| author | Chung, Cheuk T. Bazoukis, George Lee, Sharen Liu, Ying Liu, Tong Letsas, Konstantinos P. Armoundas, Antonis A. Tse, Gary |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Chung, Cheuk T. Bazoukis, George Lee, Sharen Liu, Ying Liu, Tong Letsas, Konstantinos P. Armoundas, Antonis A. Tse, Gary |
| author_sort | Chung, Cheuk T. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:47:37Z |
| format | Article |
| id | mit-1721.1/141638 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:47:37Z |
| publishDate | 2022 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1416382024-03-19T17:32:49Z Machine learning techniques for arrhythmic risk stratification: a review of the literature Chung, Cheuk T. Bazoukis, George Lee, Sharen Liu, Ying Liu, Tong Letsas, Konstantinos P. Armoundas, Antonis A. Tse, Gary Massachusetts Institute of Technology. Institute for Medical Engineering & Science 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. 2022-04-04T14:35:46Z 2022-04-04T14:35:46Z 2022-04-01 2022-04-03T03:13:33Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/141638 International Journal of Arrhythmia. 2022 Apr 01;23(1):10 PUBLISHER_CC en https://doi.org/10.1186/s42444-022-00062-2 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf BioMed Central BioMed Central |
| spellingShingle | Chung, Cheuk T. 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 |
| url | https://hdl.handle.net/1721.1/141638 |
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