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Dual Antithrombotic Therapy with Dabigatran after PCI in Atrial Fibrillation Design and rationale for the treatment effects of provisional side branch stenting and DK crush stenting techniques in patients with unprotected distal left main coronary artery bifurcation lesions (DKCRUSH V) Trial Direct comparison of cardiac myosin-binding protein C with cardiac troponins for the early diagnosis of acute myocardial infarction National Quality Assessment of Early Clopidogrel Therapy in Chinese Patients With Acute Myocardial Infarction (AMI) in 2006 and 2011: Insights From the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE)-Retrospective AMI Study Usefulness of the SYNTAX score II to validate 2-year outcomes in patients with complex coronary artery disease undergoing percutaneous coronary intervention: A large single-center study Sex Differences in Clinical Profiles and Quality of Care Among Patients With ST-Segment Elevation Myocardial Infarction From 2001 to 2011: Insights From the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE)-Retrospective Study Respiratory Syncytial Virus and Associations With Cardiovascular Disease in Adults Acute Myocardial Infarction Silent Myocardial Infarction and Long-Term Risk of Heart Failure: The ARIC Study Management of ST-segment elevation myocardial infarction in predominantly rural central China: A retrospective observational study

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.