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充血性心力衰竭

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Economic and Quality-of-Life Outcomes of Natriuretic Peptide–Guided Therapy for Heart Failure Haemodynamic-guided management of heart failure (GUIDE-HF): a randomised controlled trial Cardiac and Kidney Benefits of Empagliflozin in Heart Failure Across the Spectrum of Kidney Function: Insights From EMPEROR-Reduced The Role of the Pericardium in Heart Failure: Implications for Pathophysiology and Treatment The conductive function of biopolymer corrects myocardial scar conduction blockage and resynchronizes contraction to prevent heart failure INTERMACS Profiles and Outcomes Among Non–Inotrope-Dependent Outpatients With Heart Failure and Reduced Ejection Fraction Reduced Apolipoprotein M and Adverse Outcomes Across the Spectrum of Human Heart Failure The Future of Biomarker-Guided Therapy for Heart Failure After the Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT) Study The Evolution of β-Blockers in Coronary Artery Disease and Heart Failure (Part 1/5) Cardiovascular Events Associated With SGLT-2 Inhibitors Versus Other Glucose-Lowering Drugs: The CVD-REAL 2 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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