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充血性心力衰竭

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The Management of Atrial Fibrillation in Heart Failure: An Expert Panel Consensus Sex Differences in Cardiovascular Pathophysiology: Why Women Are Overrepresented in Heart Failure With Preserved Ejection Fraction A trial to evaluate the effect of the sodium-glucose co-transporter 2 inhibitor dapagliflozin on morbidity and mortality in patients with heart failure and reduced left ventricular ejection fraction (DAPA-HF) Modifiable lifestyle factors and heart failure: A Mendelian randomization study Atrial Fibrillation and the Prognostic Performance of Biomarkers in Heart Failure Impact of Myocardial Scar on Prognostic Implication of Secondary Mitral Regurgitation in Heart Failure Two-Year Outcomes with a Magnetically Levitated Cardiac Pump in Heart Failure Permanent pacemaker use among patients with heart failure and preserved ejection fraction: Findings from the Acute Decompensated Heart Failure National Registry (ADHERE) National Registry Dilated cardiomyopathy: so many cardiomyopathies! Heart Failure and Atrial Fibrillation, Like Fire and Fury
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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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