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Impact of percutaneous coronary intervention extent, complexity and platelet reactivity on outcomes after drug-eluting stent implantation Incidence, predictors, and outcomes of DAPT disruption due to non-compliance vs. bleeding after PCI: insights from the PARIS Registry Individualizing Revascularization Strategy for Diabetic Patients With Multivessel Coronary Disease Comparison of Accuracy of One-Use Methods for Calculating Fractional Flow Reserve by Intravascular Optical Coherence Tomography to That Determined by the Pressure-Wire Method SCAI clinical expert consensus statement on the classification of cardiogenic shock: This document was endorsed by the American College of Cardiology (ACC), the American Heart Association (AHA), the Society of Critical Care Medicine (SCCM), and the Society of Thoracic Surgeons (STS) in April 2019 Association of Coronary Anatomical Complexity With Clinical Outcomes After Percutaneous or Surgical Revascularization in the Veterans Affairs Clinical Assessment Reporting and Tracking Program Randomized Comparison Between Radial and Femoral Large-Bore Access for Complex Percutaneous Coronary Intervention The Prognostic Value of Exercise Echocardiography After Percutaneous Coronary Intervention Switching P2Y12-receptor inhibitors in patients with coronary artery disease Prevalence, Presentation and Treatment of 'Balloon Undilatable' Chronic Total Occlusions: Insights from a Multicenter US Registry
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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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