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The HACD4 haplotype as a risk factor for atherosclerosis in males De-escalation of antianginal medications after successful chronic total occlusion percutaneous coronary intervention: Frequency and relationship with health status The Impact of Proximal Vessel Tortuosity on the Outcomes of Chronic Total Occlusion Percutaneous Coronary Intervention: Insights From a Contemporary Multicenter Registry Changes in One-Year Mortality in Elderly Patients Admitted with Acute Myocardial Infarction in Relation with Early Management 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines Long-term Survival following Multivessel Revascularization in Patients with Diabetes (FREEDOM Follow-On Study) Comparison of Stent Expansion Guided by Optical Coherence Tomography Versus Intravascular Ultrasound: The ILUMIEN II Study (Observational Study of Optical Coherence Tomography [OCT] in Patients Undergoing Fractional Flow Reserve [FFR] and Percutaneous Coronary Intervention) Left Ventricular Assist Device as a Bridge to Recovery for Patients With Advanced Heart Failure Stent fracture is associated with a higher mortality in patients with type-2 diabetes treated by implantation of a second-generation drug-eluting stent Eruptive Calcified Nodules as a Potential Mechanism of Acute Coronary Thrombosis and Sudden Death

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.