Machine learning in heart failure diagnosis, prediction, and prognosis: review.

Autor:	Saqib M; Khyber Medical College, Peshawar., Perswani P; University of California Riverside, Riverside., Muneem A; College of Medicine, The Pennsylvania State University, Hershey, United States., Mumtaz H; BPP University, United Kingdom., Neha F; Jinnah Sindh Medical University, Karachi., Ali S; Saidu Medical College, Swat., Tabassum S; King Edward Medical University, Lahore, Pakistan.
Jazyk:	angličtina
Zdroj:	Annals of medicine and surgery (2012) [Ann Med Surg (Lond)] 2024 May 06; Vol. 86 (6), pp. 3615-3623. Date of Electronic Publication: 2024 May 06 (Print Publication: 2024).
DOI:	10.1097/MS9.0000000000002138
Abstrakt:	Globally, cardiovascular diseases take the lives of over 17 million people each year, mostly through myocardial infarction, or MI, and heart failure (HF). This comprehensive literature review examines various aspects related to the diagnosis, prediction, and prognosis of HF in the context of machine learning (ML). The review covers an array of topics, including the diagnosis of HF with preserved ejection fraction (HFpEF) and the identification of high-risk patients with HF with reduced ejection fraction (HFrEF). The prediction of mortality in different HF populations using different ML approaches is explored, encompassing patients in the ICU, and HFpEF patients using biomarkers and gene expression. The review also delves into the prediction of mortality and hospitalization rates in HF patients with mid-range ejection fraction (HFmrEF) using ML methods. The findings highlight the significance of a multidimensional approach that encompasses clinical evaluation, laboratory assessments, and comprehensive research to improve our understanding and management of HF. Promising predictive models incorporating biomarkers, gene expression, and consideration of epigenetics demonstrate potential in estimating mortality and identifying high-risk HFpEF patients. This literature review serves as a valuable resource for researchers, clinicians, and healthcare professionals seeking a comprehensive and updated understanding of the role of ML diagnosis, prediction, and prognosis of HF across different subtypes and patient populations. Competing Interests: No conflicts of interest.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article. (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)
Databáze:	MEDLINE
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