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Drug-Drug Interactions of Common Cardiac Medications and Chemotherapeutic Agents Randomized study of doxorubicin-based chemotherapy regimens, with and without sildenafil, with analysis of intermediate cardiac markers Short-Term Oral Anticoagulation Versus Antiplatelet Therapy Following Transcatheter Left Atrial Appendage Closure Risk of Cardiovascular Diseases Among Older Breast Cancer Survivors in the United States: A Matched Cohort Study Mathematical modelling of endovascular drug delivery: balloons versus stents High Coronary Shear Stress in Patients With Coronary Artery Disease Predicts Myocardial Infarction Thirty-Day Outcomes Following Transfemoral Transseptal Transcatheter Mitral Valve Replacement: Intrepid TMVR Early Feasibility Study Results MITRA-FR vs. COAPT: Lessons from two trials with diametrically opposed results The Art of SAPIEN 3 Transcatheter Mitral Valve Replacement in Valve-in-Ring and Valve-in-Mitral-Annular-Calcification Procedures Treatment Effects of Pulmonary Artery Denervation for Pulmonary Arterial Hypertension Stratified by REVEAL Risk Score: Results from PADN-CFDA Trial

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.