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Bypass Surgery or Stenting for Left Main Coronary Artery Disease in Patients With Diabetes Heart Failure With Preserved Ejection Fraction in the Young In vivo intravascular ultrasound-derived thin-cap fibroatheroma detection using ultrasound radiofrequency data analysis Intravascular ultrasound-guided implantation of drug-eluting stents to improve outcome: a meta-analysis Non-obstructive High-Risk Plaques Increase the Risk of Future Culprit Lesions Comparable to Obstructive Plaques Without High-Risk Features: The ICONIC Study Treatment strategies for coronary in-stent restenosis: systematic review and hierarchical Bayesian network meta-analysis of 24 randomised trials and 4880 patients The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure Left Main Revascularization With PCI or CABG in Patients With Chronic Kidney Disease: EXCEL Trial Patient Selection and Clinical Outcomes in the STOPDAPT-2 Trial: An All-Comer Single-Center Registry During the Enrollment Period of the STOPDAPT-2 Randomized Controlled Trial Use of Intravascular Ultrasound Imaging in Percutaneous Coronary Intervention to Treat Left Main Coronary Artery 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.