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充血性心力衰竭

科研文章

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Effects of Liraglutide on Cardiovascular Outcomes in Patients With Diabetes With or Without Heart Failure Effect of empagliflozin on exercise ability and symptoms in heart failure patients with reduced and preserved ejection fraction, with and without type 2 diabetes Is Acute heart failure a distinctive disorder? An analysis from BIOSTAT-CHF 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 Antithrombotics From Aspirin to DOACs in Coronary Artery Disease and Atrial Fibrillation (Part 3/5) The Hospital Readmissions Reduction Program Nationwide Perspectives and Recommendations: A JACC: Heart Failure Position Paper Diagnosis of Nonischemic Stage B Heart Failure in Type 2 Diabetes Mellitus: Optimal Parameters for Prediction of Heart Failure Diuretic Therapy for Patients With Heart Failure JACC State-of-the-Art Review A Fully Magnetically Levitated Left Ventricular Assist Device — Final Report Angiotensin–neprilysin inhibition versus enalapril in heart failure

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.