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充血性心力衰竭

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Association of Abnormal Left Ventricular Functional Reserve With Outcome in Heart Failure With Preserved Ejection Fraction Permanent pacemaker use among patients with heart failure and preserved ejection fraction: Findings from the Acute Decompensated Heart Failure National Registry (ADHERE) National Registry Association Between Functional Impairment and Medication Burden in Adults with Heart Failure Aliskiren, Enalapril, or Aliskiren and Enalapril in Heart Failure Rationale and design of the GUIDE-IT study: Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure Angiotensin–Neprilysin Inhibition in Heart Failure with Preserved Ejection Fraction Universal Definition and Classification of Heart Failure: A Report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure Heart Failure and Atrial Fibrillation, Like Fire and Fury The Management of Atrial Fibrillation in Heart Failure: An Expert Panel Consensus Sex- and Race-Related Differences in Characteristics and Outcomes of Hospitalizations for Heart Failure With Preserved Ejection Fraction
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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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