CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Temporal trends in percutaneous coronary interventions thru the drug eluting stent era: Insights from 18,641 procedures performed over 12-year period The (R)Evolution of the CICU - Better for the Patient, Better for Education Balloon-to-door time: emerging evidence for shortening hospital stay after primary PCI for STEMI Incidence, Treatment, and Outcomes of Coronary Perforation During Chronic Total Occlusion Percutaneous Coronary Intervention Trends in early aspirin use among patients with acute myocardial infarction in China, 2001-2011: the China PEACE-Retrospective AMI study Response by Kaier et al to Letter Regarding Article, “Direct Comparison of Cardiac Myosin-Binding Protein C With Cardiac Troponins for the Early Diagnosis of Acute Myocardial Infarction” Early Versus Standard Discharge After Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis Letter by Jiang et al Regarding Article, “Direct Comparison of Cardiac Myosin-Binding Protein C With Cardiac Troponins for the Early Diagnosis of Acute Myocardial Infarction” The China Patient-centered Evaluative Assessment of Cardiac Events (PEACE) Prospective Study of Percutaneous Coronary Intervention: Study Design Door-to-balloon time and mortality among patients undergoing primary PCI
|<<... 12 13 14 15 16 17 18 ...>>|

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.

>CBS CBS 2019

> CBS 2019

> CBS 2019 注册

> CBS 2019 会议日程

> CBS 2019 学术征集

> CBS 2019 热点

 CBSMD

> CBSMD 网站注册

> 教育中心

> 荐读文献

> Top 10 阅读文献

> 文献导读
CBS 2019 CBS 2019 主席团 教育中心 科研动态
Copyright ⓒ CBS MD Nanjing China. All rights reserved. 京ICP备16025898号-11
联系我们| Top