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Ticagrelor Monotherapy Versus Ticagrelor With Aspirin in Patients With ST-Segment Elevation Myocardial Infarction Dual-Antiplatelet Therapy Cessation and Cardiovascular Risk in Relation to Age: Analysis From the PARIS Registry Ticagrelor With or Without Aspirin in High-Risk Patients With Diabetes Mellitus Undergoing Percutaneous Coronary Intervention Low Endothelial Shear Stress Predicts Evolution to High-Risk Coronary Plaque Phenotype in the Future: A Serial Optical Coherence Tomography and Computational Fluid Dynamics Study Guideline Update on Indications for Transcatheter Aortic Valve Implantation Based on the 2020 American College of Cardiology/American Heart Association Guidelines for Management of Valvular Heart Disease Pooled Analysis of Bleeding, Major Adverse Cardiovascular Events, and All-Cause Mortality in Clinical Trials of Time-Constrained Dual-Antiplatelet Therapy After Percutaneous Coronary Intervention Stroke Complicating Infective Endocarditis After Transcatheter Aortic Valve Replacement Diagnostic Accuracy of Angiography-Based Quantitative Flow Ratio Measurements for Online Assessment of Coronary Stenosis Antithrombotic Management of Elderly Patients With Coronary Artery Disease Intravascular ultrasound in the evaluation and treatment of left main coronary artery disease: a consensus statement from the European Bifurcation Club

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.