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Double-Kiss-Crush Bifurcation Stenting: Step-by-Step Troubleshooting Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the International Working Group for Intravascular Optical Coherence Tomography Standardization and Validation Uncovered Culprit Plaque Ruptures in Patients With ST-Segment Elevation Myocardial Infarction Assessed by Optical Coherence Tomography and Intravascular Ultrasound With iMap Covering our tracks – optical coherence tomography to assess vascular healing Chronic thromboembolic pulmonary hypertension Meta-Analysis of Death and Myocardial Infarction in the DEFINE-FLAIR and iFR-SWEDEHEART Trials Incidence of Adverse Events at 3 Months Versus at 12 Months After Dual Antiplatelet Therapy Cessation in Patients Treated With Thin Stents With Unprotected Left Main or Coronary Bifurcations Comparison of Coronary Intimal Plaques by Optical Coherence Tomography in Arteries With Versus Without Internal Running Vasa Vasorum Device specificity of vascular healing following implantation of bioresorbable vascular scaffolds and bioabsorbable polymer metallic drug-eluting stents in human coronary arteries: the ESTROFA OCT BVS vs. BP-DES study Elaborately Engineering a Self-Indicating Dual-Drug Nanoassembly for Site-Specific Photothermal-Potentiated Thrombus Penetration and Thrombolysis

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.