CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Positive remodeling at 3 year follow up is associated with plaque-free coronary wall segment at baseline: a serial IVUS study The role of integrated backscatter intravascular ultrasound in characterizing bare metal and drug-eluting stent restenotic neointima as compared to optical coherence tomography A Prospective, Multicenter, Randomized, Open-label Trial to Compare Efficacy and Safety of Clopidogrel vs. Ticagrelor in Stabilized Patients with Acute Myocardial Infarction after Percutan eous Coronary Intervention: rationale and design of the TALOS-AMI trial Comparison of newer generation self-expandable vs. balloon-expandable valves in transcatheter aortic valve implantation: the randomized SOLVE-TAVI trial Association of White Matter Hyperintensities and Cardiovascular Disease: The Importance of Microcirculatory Disease Comparison of 1-Year Pre- And Post-Transcatheter Aortic Valve Replacement Hospitalization Rates: A Population-Based Cohort Study Edoxaban versus Vitamin K Antagonist for Atrial Fibrillation after TAVR Impact of Staging Percutaneous Coronary Intervention in Left Main Artery Disease: Insights From the EXCEL Trial Valve‐in‐Valve for Degenerated Transcatheter Aortic Valve Replacement Versus Valve‐in‐Valve for Degenerated Surgical Aortic Bioprostheses: A 3‐Center Comparison of Hemodynamic and 1‐Year Outcome Right ventricular function and outcome in patients undergoing transcatheter aortic valve replacement
|<<... 113 114 115 116 117 118 119 ...>>|

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