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充血性心力衰竭

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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 Baseline Features of the VICTORIA (Vericiguat Global Study in Subjects With Heart Failure With Reduced Ejection Fraction) Trial Angiotensin–Neprilysin Inhibition in Heart Failure with Preserved Ejection Fraction A Fully Magnetically Levitated Circulatory Pump for Advanced Heart Failure Primary Prevention of Heart Failure in Women Modifiable lifestyle factors and heart failure: A Mendelian randomization study Heart Failure and Atrial Fibrillation, Like Fire and Fury The Management of Atrial Fibrillation in Heart Failure: An Expert Panel Consensus Aliskiren, Enalapril, or Aliskiren and Enalapril in Heart Failure Nocturnal thoracic volume overload and post-discharge outcomes in patients hospitalized for acute heart failure
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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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