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充血性心力衰竭

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Frequency, predictors, and prognosis of ejection fraction improvement in heart failure: an echocardiogram-based registry study Nuclear Imaging of the Cardiac Sympathetic Nervous System: A Disease-Specific Interpretation in Heart Failure SGLT-2 Inhibitors and Cardiovascular Risk: An Analysis of CVD-REAL Association of Prior Left Ventricular Ejection Fraction With Clinical Outcomes in Patients With Heart Failure With Midrange Ejection Fraction The Future of Biomarker-Guided Therapy for Heart Failure After the Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT) Study Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery Two-Year Outcomes with a Magnetically Levitated Cardiac Pump in Heart Failure Circulating sST2 and catestatin levels in patients with acute worsening of heart failure: a report from the CATSTAT-HF study Effect of Luseogliflozin on Heart Failure With Preserved Ejection Fraction in Patients With Diabetes Mellitus Cardiac Resynchronization Therapy in Inotrope-Dependent Heart Failure Patients - A Systematic Review and Meta-Analysis
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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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