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Contrast-Associated Acute Kidney Injury and Serious Adverse Outcomes Following Angiography Association of CYP2C19 Loss-of-Function Alleles with Major Adverse Cardiovascular Events of Clopidogrel in Stable Coronary Artery Disease Patients Undergoing Percutaneous Coronary Intervention: Meta-analysis Incidence, Predictors, and Outcomes of In-Hospital Percutaneous Coronary Intervention Following Coronary Artery Bypass Grafting Short-term and long-term clinical outcomes of rotational atherectomy in resistant chronic total occlusion 2020 AHA/ACC Key Data Elements and Definitions for Coronary Revascularization A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Coronary Revascularization) Causes, Timing, and Impact of Dual Antiplatelet Therapy Interruption for Surgery (from the Patterns of Non-adherence to Anti-platelet Regimens In Stented Patients Registry) Five-Year Outcomes after PCI or CABG for Left Main Coronary Disease Mortality Differences Associated With Treatment Responses in CANTOS and FOURIER: Insights and Implications Improving the Design of Future PCI Trials for Stable Coronary Artery Disease: JACC State-of-the-Art Review Drug-Coated Balloon Versus Drug-Eluting Stent in Primary Percutaneous Coronary Intervention: A Feasibility Study
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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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