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充血性心力衰竭

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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 Antithrombotics From Aspirin to DOACs in Coronary Artery Disease and Atrial Fibrillation (Part 3/5) Association of loop diuretics use and dose with outcomes in outpatients with heart failure: a systematic review and meta-analysis of observational studies involving 96,959 patients Is Acute heart failure a distinctive disorder? An analysis from BIOSTAT-CHF 3D Printing and Heart Failure: The Present and the Future SGLT-2 Inhibitors and Cardiovascular Risk: An Analysis of CVD-REAL A pragmatic approach to the use of inotropes for the management of acute and advanced heart failure: An expert panel consensus A randomized controlled trial to evaluate the safety and efficacy of cardiac contractility modulation in patients with systolic heart failure: rationale, design, and baseline patient characteristics. The Hospital Readmissions Reduction Program Nationwide Perspectives and Recommendations: A JACC: Heart Failure Position Paper

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.