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Abnormalities in 3-Dimensional Left Ventricular Mechanics With Anthracycline Chemotherapy Are Associated With Systolic and Diastolic Dysfunction Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings Feasibility and efficacy of the ultrashort side branch dedicated balloon in coronary bifurcation stenting Impact of large periprocedural myocardial infarction on mortality after percutaneous coronary intervention and coronary artery bypass grafting for left main disease: an analysis from the EXCEL trial 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS): The Task Force for the diagnosis and management of acute pulmonary embolism of the European Society of Cardiology (ESC) OCT guidance during stent implantation in primary PCI: A randomized multicenter study with nine months of optical coherence tomography follow-up Flow-Regulated Endothelial S1P Receptor-1 Signaling Sustains Vascular Development Coronary Optical Coherence Tomography and Cardiac Magnetic Resonance Imaging to Determine Underlying Causes of Myocardial Infarction With Nonobstructive Coronary Arteries in Women Instantaneous Wave-free Ratio versus Fractional Flow Reserve to Guide PCI Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease

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.