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Percutaneous coronary intervention with drug-eluting stents versus coronary artery bypass grafting in left main coronary artery disease: an individual patient data meta-analysis A Survey on Coronary Atherosclerotic Plaque Tissue Characterization in Intravascular Optical Coherence Tomography Cellular origin and developmental program of coronary angiogenesis Pulmonary Hypertension Caused by a Coconut Left Atrium A randomized comparison of Coronary Stents according to Short or Prolonged durations of Dual Antiplatelet Therapy in patients with Acute Coronary Syndromes: a pre-specified analysis of the SMART-DATE trial Medical Therapy for CTEPH: Is There Still Space for More? Pulmonary Artery Denervation for Patients With Residual Pulmonary Hypertension After Pulmonary Endarterectomy Pulmonary hypertension related to congenital heart disease: a call for action Evaluation and Management of Aortic Stenosis in Chronic Kidney Disease: A Scientific Statement From the American Heart Association Dual Antiplatelet Therapy after PCI in Patients at High Bleeding Risk

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.