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Intravascular ultrasound enhances the safety of rotational atherectomy Pulmonary hypertension is associated with an increased incidence of NAFLD: A retrospective cohort study of 18,910 patients Clinical Characteristics and Long-Term Outcomes of Rotational Atherectomy-J2T Multicenter Registry Percutaneous Treatment and Outcomes of Small Coronary Vessels: A SCAAR Report North American Expert Review of Rotational Atherectomy Procedural Success and Outcomes With Increasing Use of Enabling Strategies for Chronic Total Occlusion Intervention Orbital atherectomy for the treatment of small (2.5mm) severely calcified coronary lesions: ORBIT II sub-analysis The Regulation of Pulmonary Vascular Tone by Neuropeptides and the Implications for Pulmonary Hypertension Coronary Calcification and Long-Term Outcomes According to Drug-Eluting Stent Generation Trends in Usage and Clinical Outcomes of Coronary Atherectomy: A Report From the National Cardiovascular Data Registry CathPCI Registry
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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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