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Unexpectedly Low Natriuretic Peptide Levels in Patients With Heart Failure Guideline‐Directed Medical Therapy for Patients With Heart Failure With Midrange Ejection Fraction: A Patient‐Pooled Analysis From the KorHF and KorAHF Registries Baseline Features of the VICTORIA (Vericiguat Global Study in Subjects With Heart Failure With Reduced Ejection Fraction) Trial Atrial Fibrillation and the Prognostic Performance of Biomarkers in Heart Failure From ACE Inhibitors/ARBs to ARNIs in Coronary Artery Disease and Heart Failure (Part 2/5) Mechanical circulatory support devices for acute right ventricular failure Rationale and design of the GUIDE-IT study: Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure The year in cardiology: heart failure: The year in cardiology 2019 Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure Natriuretic Peptide-Guided Heart Failure Therapy After the GUIDE-IT Study
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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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