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Randomized study on simple versus complex stenting of coronary artery bifurcation lesions: the Nordic bifurcation study Optical Coherence Tomography Predictors for Recurrent Restenosis After Paclitaxel-Coated Balloon Angioplasty for Drug-Eluting Stent Restenosis The European bifurcation club Left Main Coronary Stent study: a randomized comparison of stepwise provisional vs. systematic dual stenting strategies (EBC MAIN) Angiographic quantitative flow ratio-guided coronary intervention (FAVOR III China): a multicentre, randomised, sham-controlled trial Comparison of the safety and efficacy of two types of drug-eluting balloons (RESTORE DEB and SeQuent® Please) in the treatment of coronary in-stent restenosis: study protocol for a randomized controlled trial (RESTORE ISR China) In-Hospital Outcomes of Chronic Total Occlusion Percutaneous Coronary Interventions in Patients With Prior Coronary Artery Bypass Graft Surgery Multicentre, randomized comparison of two-stent and provisional stenting techniques in patients with complex coronary bifurcation lesions: the DEFINITION II trial Comparison of new-generation drug-eluting stents versus drug-coated balloon for in-stent restenosis: a meta-analysis of randomised controlled trials Sustainable Antirestenosis Effect With a Low-Dose Drug-Coated Balloon: The ILLUMENATE European Randomized Clinical Trial 2-Year Results Long-term clinical outcomes after treatment of stent restenosis with two drug-coated balloons
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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.

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