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Randomized Evaluation of Heart Failure With Preserved Ejection Fraction Patients With Acute Heart Failure and Dopamine - The ROPA-DOP Trial 2021 ACC/AHA Key Data Elements and Definitions for Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Heart Failure) Differential Impact of Heart Failure With Reduced Ejection Fraction on Men and Women Association of Reduced Apical Untwisting With Incident HF in Asymptomatic Patients With HF Risk Factors Proteomics to Improve Phenotyping in Obese Patients with Heart Failure with Preserved Ejection Fraction Lateral Wall Dysfunction Signals Onset of Progressive Heart Failure in Left Bundle Branch Block Haemodynamic-guided management of heart failure (GUIDE-HF): a randomised controlled trial Clinical Phenogroups in Heart Failure With Preserved Ejection Fraction: Detailed Phenotypes, Prognosis, and Response to Spironolactone Criteria for Iron Deficiency in Patients With Heart Failure 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines
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Review Article2020 Jul 16;229:1-17.

JOURNAL：Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

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