CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Contemporary Use and Trends in Unprotected Left Main Coronary Artery Percutaneous Coronary Intervention in the United States: An Analysis of the National Cardiovascular Data Registry Research to Practice Initiative Impact of intravascular ultrasound guidance in routine percutaneous coronary intervention for conventional lesions: data from the EXCELLENT trial Global Approach to High Bleeding Risk Patients With Polymer-Free Drug-Coated Coronary Stents: The LF II Study Stent underexpansion and residual reference segment stenosis are related to stent thrombosis after sirolimus-eluting stent implantation: an intravascular ultrasound study Impact of SYNTAX Score on 10-Year Outcomes After Revascularization for Left Main Coronary Artery Disease A risk score to predict postdischarge bleeding among acute coronary syndrome patients undergoing percutaneous coronary intervention: BRIC-ACS study When, where, and how to target vascular inflammation in the post-CANTOS era? Efficacy and safety of low-dose colchicine in patients with coronary disease: a systematic review and meta-analysis of randomized trials 2019 Guidelines on Diabetes, Pre-Diabetes and Cardiovascular Diseases developed in collaboration with the EASD ESC Clinical Practice Guidelines Post-stenting fractional flow reserve vs coronary angiography for optimisation of percutaneous coronary intervention: TARGET-FFR trial
|<<... 125 126 127 128 129 130 131 ...>>|

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