CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Management of Myocardial Revascularization Failure: An Expert Consensus Document of the EAPCI Intracoronary Optical Coherence Tomography-Derived Virtual Fractional Flow Reserve for the Assessment of Coronary Artery Disease Coronary Artery Calcium Progression Is Associated With Coronary Plaque Volume Progression - Results From a Quantitative Semiautomated Coronary Artery Plaque Analysis The year in cardiovascular medicine 2020: interventional cardiology A randomised trial comparing two stent sizing strategies in coronary bifurcation treatment with bioresorbable vascular scaffolds - The Absorb Bifurcation Coronary (ABC) trial Association of preoperative glucose concentration with myocardial injury and death after non-cardiac surgery (GlucoVISION): a prospective cohort study Prognostic Value of the Residual SYNTAX Score After Functionally Complete Revascularization in ACS Prognostic value of fibrinogen in patients with coronary artery disease and prediabetes or diabetes following percutaneous coronary intervention: 5-year findings from a large cohort study Syncope After Percutaneous Coronary Intervention Heart Regeneration by Endogenous Stem Cells and Cardiomyocyte Proliferation: Controversy, Fallacy, and Progress
|<<... 29 30 31 32 33 34 35 ...>>|

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