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充血性心力衰竭

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A Fully Magnetically Levitated Left Ventricular Assist Device — Final Report In acute HF and iron deficiency, IV ferric carboxymaltose reduced HF hospitalizations, but not CV death, at 1 y Sex Differences in Cardiovascular Pathophysiology: Why Women Are Overrepresented in Heart Failure With Preserved Ejection Fraction The Hospital Readmissions Reduction Program Nationwide Perspectives and Recommendations: A JACC: Heart Failure Position Paper Rationale and design of the comParIson Of sacubitril/valsartaN versus Enalapril on Effect on nt-pRo-bnp in patients stabilized from an acute Heart Failure episode (PIONEER-HF) trial Positive recommendation for angiotensin receptor/neprilysin inhibitor: First medication approval for heart failure without "reduced ejection fraction" A Fully Magnetically Levitated Circulatory Pump for Advanced Heart Failure Clinical epidemiology of heart failure with preserved ejection fraction (HFpEF) in comparatively young hospitalized patients Association of Prior Left Ventricular Ejection Fraction With Clinical Outcomes in Patients With Heart Failure With Midrange Ejection Fraction Circulating sST2 and catestatin levels in patients with acute worsening of heart failure: a report from the CATSTAT-HF study

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.