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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 Economic and Quality-of-Life Outcomes of Natriuretic Peptide–Guided Therapy for Heart Failure Angiotensin–neprilysin inhibition versus enalapril in heart failure Diagnosis of Nonischemic Stage B Heart Failure in Type 2 Diabetes Mellitus: Optimal Parameters for Prediction of Heart Failure Stage B heart failure: management of asymptomatic left ventricular systolic dysfunction SGLT2 Inhibitors in Patients With Heart Failure With Reduced Ejection Fraction: A Meta-Analysis of the EMPEROR-Reduced and DAPA-HF Trials Glucose-lowering Drugs or Strategies, Atherosclerotic Cardiovascular Events, and Heart Failure in People With or at Risk of Type 2 Diabetes: An Updated Systematic Review and Meta-Analysis of Randomised Cardiovascular Outcome Trials Effect of empagliflozin on exercise ability and symptoms in heart failure patients with reduced and preserved ejection fraction, with and without type 2 diabetes 3D Printing and Heart Failure: The Present and the Future Fluid Volume Overload and Congestion in Heart Failure: Time to Reconsider Pathophysiology and How Volume Is Assessed
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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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