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Dual Antiplatelet Therapy Duration in Medically Managed Acute Coronary Syndrome Patients: Sub-Analysis of the OPT-CAD Study Why NOBLE and EXCEL Are Consistent With Each Other and With Previous Trials Differential prognostic effect of intravascular ultrasound use according to implanted stent length Transcatheter Aortic Valve Replacement: Role of Multimodality Imaging in Common and Complex Clinical Scenarios 1-Year Outcomes of Delayed Versus Immediate Intervention in Patients With Transient ST-Segment Elevation Myocardial Infarction Nonculprit Lesion Plaque Morphology in Patients With ST-Segment–Elevation Myocardial Infarction: Results From the COMPLETE Trial Optical Coherence Tomography Substudys 5-Year Outcomes After TAVR With Balloon-Expandable Versus Self-Expanding Valves: Results From the CHOICE Randomized Clinical Trial Association of Sustained Blood Pressure Control with Multimorbidity Progression Among Older Adults Accuracy of Fractional Flow Reserve Derived From Coronary Angiography Differential Impact of Heart Failure With Reduced Ejection Fraction on Men and Women

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.