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Definition and Management of Segmental Pulmonary Hypertension Left atrial appendage occlusion in atrial fibrillation patients with previous intracranial bleeding: A national multicenter study Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease Incidence and Management of Restenosis After Treatment of Unprotected Left Main Disease With Second-Generation Drug-Eluting Stents (from Failure in Left Main Study With 2nd Generation Stents-Cardiogroup III Study) Pancoronary Plaque Characteristics in STEMI Caused by Culprit Plaque Erosion Versus Rupture: 3-Vessel OCT Study Low shear stress induces vascular eNOS uncoupling via autophagy-mediated eNOS phosphorylation Radial versus femoral artery access in patients undergoing PCI for left main coronary artery disease: analysis from the EXCEL trial Transthoracic echocardiography for the evaluation of children and adolescents with suspected or confirmed pulmonary hypertension. Expert consensus statement on the diagnosis and treatment of paediatric pulmonary hypertension. The European Paediatric Pulmonary Vascular Disease Network, endorsed by ISHLT and D6PK Access Site and Outcomes for Unprotected Left Main Stem Percutaneous Coronary Intervention: An Analysis of the British Cardiovascular Intervention Society Database ACCF/AHA 2009 expert consensus document on pulmonary hypertension a report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents and the American Heart Association developed in collaboration with the American College of Chest Physicians; American Thoracic Society, Inc.; and the Pulmonary Hypertension Association
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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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