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From Early Pharmacology to Recent Pharmacology Interventions in Acute Coronary Syndromes Percutaneous Coronary Intervention for Chronic Total Occlusion—The Michigan Experience: Insights From the BMC2 Registry Epidemiology and Clinical Outcomes of Patients With Inflammatory Bowel Disease Presenting With Acute Coronary Syndrome The Prognostic Significance of Periprocedural Infarction in the Era of Potent Antithrombotic Therapy: The PRAGUE-18 Substudy Transition of Macrophages to Fibroblast-Like Cells in Healing Myocardial Infarction Linking Spontaneous Coronary Artery Dissection, Cervical Artery Dissection, and Fibromuscular Dysplasia: Heart, Brain, and Kidneys Comparative Effectiveness of β-Blocker Use Beyond 3 Years After Myocardial Infarction and Long-Term Outcomes Among Elderly Patients Intensive Care Utilization in Stable Patients With ST-Segment Elevation Myocardial Infarction Treated With Rapid Reperfusion Percutaneous coronary intervention for coronary bifurcation disease: 11th consensus document from the European Bifurcation Club Another Nail in the Coffin for Intra-Aortic Balloon Counterpulsion in Acute Myocardial Infarction With Cardiogenic Shock
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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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