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Evolving insights into the role of local shear stress in late stent failure from neoatherosclerosis formation and plaque destabilization Functional Mitral Regurgitation Outcome and Grading in Heart Failure With Reduced Ejection Fraction Strain-Guided Management of Potentially Cardiotoxic Cancer Therapy Hemodynamic, Functional, and Clinical Responses to Pulmonary Artery Denervation in Patients With Pulmonary Arterial Hypertension of Different Causes Management and outcomes of patients with left atrial appendage thrombus prior to percutaneous closure Rivaroxaban for Thromboprophylaxis in High-Risk Ambulatory Patients With Cancer Long-Term Outcomes of Patients With Mediastinal Radiation–Associated Coronary Artery Disease Undergoing Coronary Revascularization With Percutaneous Coronary Intervention and Coronary Artery Bypass Grafting Transseptal puncture versus patent foramen ovale or atrial septal defect access for left atrial appendage closure Implications of the local hemodynamic forces on the formation and destabilization of neoatherosclerotic lesions Potential Candidates for Transcatheter Tricuspid Valve Intervention After Transcatheter Aortic Valve Replacement: Predictors and Prognosis
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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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