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Intravascular ultrasound-guided drug-eluting stent implantation is associated with improved clinical outcomes in patients with unstable angina and complex coronary artery true bifurcation lesions Rotational atherectomy and new-generation drug-eluting stent implantation Two-Year Outcomes and Predictors of Target Lesion Revascularization for Non-Left Main Coronary Bifurcation Lesions Following Two-Stent Strategy With 2nd-Generation Drug-Eluting Stents Nonproportional Hazards for Time-to-Event Outcomes in Clinical Trials: JACC Review Topic of the Week Ticagrelor with or without Aspirin in High-Risk Patients after PCI Vascular response and healing profile of everolimus-eluting bioresorbable vascular scaffolds for percutaneous treatment of chronic total coronary occlusions: A one-year optical coherence tomography analysis from the GHOST-CTO registry Timing and Causes of Unplanned Readmissions After Percutaneous Coronary Intervention: Insights From the Nationwide Readmission Database Impact of Statins on Cardiovascular Outcomes Following Coronary Artery Calcium Scoring Comparative Accuracy of Focused Cardiac Ultrasonography and Clinical Examination for Left Ventricular Dysfunction and Valvular Heart Disease: A Systematic Review and Meta-analysis Rare Genetic Variants Associated With Sudden Cardiac Death in Adults

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.