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Empagliflozin Increases Cardiac Energy Production in Diabetes - Novel Translational Insights Into the Heart Failure Benefits of SGLT2 Inhibitors Effect of SGLT2-Inhibitors on Epicardial Adipose Tissue: A Meta-Analysis INTERMACS Profiles and Outcomes Among Non–Inotrope-Dependent Outpatients With Heart Failure and Reduced Ejection Fraction Lifestyle Modifications for Preventing and Treating 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 Cardiac and Kidney Benefits of Empagliflozin in Heart Failure Across the Spectrum of Kidney Function: Insights From EMPEROR-Reduced Longitudinal Change in Galectin-3 and Incident Cardiovascular Outcomes Economic and Quality-of-Life Outcomes of Natriuretic Peptide–Guided Therapy for Heart Failure Impact of epicardial adipose tissue on cardiovascular haemodynamics, metabolic profile, and prognosis in heart failure The Evolution of β-Blockers in Coronary Artery Disease and Heart Failure (Part 1/5)
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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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