CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

European Bifurcation Club White Paper on Stenting Techniques for Patients With Bifurcated Coronary Artery Lesions Best Practices for the Prevention of Radial Artery Occlusion After Transradial Diagnostic Angiography and Intervention An International Consensus Paper Basic Biology of Oxidative Stress and the Cardiovascular System: Part 1 of a 3-Part Series Early Diagnosis of Myocardial Infarction With Point-of-Care High-Sensitivity Cardiac Troponin I 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure Discharge Against Medical Advice After Percutaneous Coronary Intervention in the United States The Astronaut Cardiovascular Health and Risk Modification (Astro-CHARM) Coronary Calcium Atherosclerotic Cardiovascular Disease Risk Calculator Validation of High-Risk Features for Stent-Related Ischemic Events as Endorsed by the 2017 DAPT Guidelines Myocardial Infarction in Young Women Impact of Optimal Medical Therapy on 10-Year Mortality After Coronary Revascularization
|<<... 46 47 48 49 50 51 52 ...>>|

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