CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Impact of intravascular ultrasound on the long-term clinical outcomes in the treatment of coronary ostial lesions Design and rationale for a randomised comparison of everolimus-eluting stents and coronary artery bypass graft surgery in selected patients with left main coronary artery disease: the EXCEL trial The relationship between attenuated plaque identified by intravascular ultrasound and no-reflow after stenting in acute myocardial infarction: the HORIZONS-AMI (Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction) trial Impact of Intravascular Ultrasound-Guided Percutaneous Coronary Intervention on Long-Term Clinical Outcomes in Patients Undergoing Complex Procedures IVUS in bifurcation stenting: what have we learned? Stopping or continuing clopidogrel 12 months after drug-eluting stent placement: the OPTIDUAL randomized trial Clinical use of intracoronary imaging. Part 1: guidance and optimization of coronary interventions. An expert consensus document of the European Association of Percutaneous Cardiovascular Interventions: Endorsed by the Chinese Society of Cardiology Novel predictor of target vessel revascularization after coronary stent implantation: Intraluminal intensity of blood speckle on intravascular ultrasound The Year in Cardiovascular Medicine 2020: Imaging: Looking back on the Year in Cardiovascular Medicine for 2020 in the field of imaging are Fausto Pinto, José Luis Zamorano and Chiara Bucciarelli-Ducci. Judy Ozkan speaks with them Optical coherence tomography and intravascular ultrasound assessment of the anatomic size and wall thickness of a muscle bridge segment
|<<... 82 83 84 85 86 87 88 ...>>|

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