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Applications of artificial intelligence and machine learning in heart failure

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to h...

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Autores principales: Averbuch, Tauben, Sullivan, Kristen, Sauer, Andrew, Mamas, Mamas A, Voors, Adriaan A, Gale, Chris P, Metra, Marco, Ravindra, Neal, Van Spall, Harriette G C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707916/
https://www.ncbi.nlm.nih.gov/pubmed/36713018
http://dx.doi.org/10.1093/ehjdh/ztac025
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author Averbuch, Tauben
Sullivan, Kristen
Sauer, Andrew
Mamas, Mamas A
Voors, Adriaan A
Gale, Chris P
Metra, Marco
Ravindra, Neal
Van Spall, Harriette G C
author_facet Averbuch, Tauben
Sullivan, Kristen
Sauer, Andrew
Mamas, Mamas A
Voors, Adriaan A
Gale, Chris P
Metra, Marco
Ravindra, Neal
Van Spall, Harriette G C
author_sort Averbuch, Tauben
collection PubMed
description Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.
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spelling pubmed-97079162023-01-27 Applications of artificial intelligence and machine learning in heart failure Averbuch, Tauben Sullivan, Kristen Sauer, Andrew Mamas, Mamas A Voors, Adriaan A Gale, Chris P Metra, Marco Ravindra, Neal Van Spall, Harriette G C Eur Heart J Digit Health Review Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur. Oxford University Press 2022-05-13 /pmc/articles/PMC9707916/ /pubmed/36713018 http://dx.doi.org/10.1093/ehjdh/ztac025 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Review
Averbuch, Tauben
Sullivan, Kristen
Sauer, Andrew
Mamas, Mamas A
Voors, Adriaan A
Gale, Chris P
Metra, Marco
Ravindra, Neal
Van Spall, Harriette G C
Applications of artificial intelligence and machine learning in heart failure
title Applications of artificial intelligence and machine learning in heart failure
title_full Applications of artificial intelligence and machine learning in heart failure
title_fullStr Applications of artificial intelligence and machine learning in heart failure
title_full_unstemmed Applications of artificial intelligence and machine learning in heart failure
title_short Applications of artificial intelligence and machine learning in heart failure
title_sort applications of artificial intelligence and machine learning in heart failure
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707916/
https://www.ncbi.nlm.nih.gov/pubmed/36713018
http://dx.doi.org/10.1093/ehjdh/ztac025
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