CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Intravascular Ultrasound-Guided Versus Angiography-Guided Implantation of Drug-Eluting Stent in All-Comers: The ULTIMATE trial Mediterranean Diet and the Association Between Air Pollution and Cardiovascular Disease Mortality Risk In patients with stable coronary heart disease, low-density lipoprotein-cholesterol levels < 70 mg/dL and glycosylated hemoglobin A1c < 7% are associated with lower major cardiovascular events Coronary calcification in the diagnosis of coronary artery disease Clinical Risk Factors and Atherosclerotic Plaque Extent to Define Risk for Major Events in Patients Without Obstructive Coronary Artery Disease: The Long-Term Coronary Computed Tomography Angiography CONFIRM Registry Bioprosthetic valve oversizing is associated with increased risk of valve thrombosis following TAVR Minimizing Permanent Pacemaker Following Repositionable Self-Expanding Transcatheter Aortic Valve Replacement Health Status after Transcatheter vs. Surgical Aortic Valve Replacement in Low-Risk Patients with Aortic Stenosis Bridging the Gap Between Epigenetic and Genetic in PAH The Role of Vascular Imaging in Guiding Routine Percutaneous Coronary Interventions: A Meta-Analysis of Bare Metal Stent and Drug-Eluting Stent Trials
|<<... 88 89 90 91 92 93 94 ...>>|

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