CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

A Meta-Analysis of Contemporary Lesion Modification Strategies During Percutaneous Coronary Intervention in 244,795 Patients From 22 Studies Drug-Coated Balloon Treatment for Femoropopliteal Artery Disease: The IN.PACT Global Study De Novo In-Stent Restenosis Imaging Cohort Coronary Artery Calcium Is Associated with Left Ventricular Diastolic Function Independent of Myocardial Ischemia Can the Vanishing Stent Reappear? Fix the Technique, or Fix the Device? Aggressive lipid-lowering therapy after percutaneous coronary intervention – for whom and how? Rotational Atherectomy Followed by Drug-Coated Balloon Dilation for Left Main In-Stent Restenosis in the Setting of Acute Coronary Syndrome Complicated with Right Coronary Chronic Total Occlusion SGLT2 Inhibitors in Patients With Heart Failure With Reduced Ejection Fraction: A Meta-Analysis of the EMPEROR-Reduced and DAPA-HF Trials Disrupting Fellow Education Through Group Texting: WhatsApp in Fellow Education? AIM2-driven inflammasome activation in heart failure Coronary Angiography after Cardiac Arrest — The Right Timing or the Right Patients?

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.