CBS 2019
CBSMD教育中心
English

科学研究

科研文章

荐读文献

Alcohol consumption, cardiac biomarkers, and risk of atrial fibrillation and adverse outcomes Detection of Device-Related Thrombosis Following Left Atrial Appendage Occlusion A Comparison Between Cardiac Computed Tomography and Transesophageal Echocardiography​: A Comparison Between Cardiac Computed Tomography and Transesophageal Echocardiography Frailty and Clinical Outcomes of Direct Oral Anticoagulants Versus Warfarin in Older Adults With Atrial Fibrillation: A Cohort Study Gut microbiota dysbiosis promotes age-related atrial fibrillation by lipopolysaccharide and glucose-induced activation of NLRP3-inflammasome Left Atrial Appendage Occlusion during Cardiac Surgery to Prevent Stroke Left Atrial Appendage Closure versus Non-Warfarin Oral Anticoagulation in Atrial Fibrillation: 4-Year Outcomes of PRAGUE-17 Patent Foramen Ovale Attributable Cryptogenic Embolism With Thrombophilia Has Higher Risk for Recurrence and Responds to Closure Stretch-induced sarcoplasmic reticulum calcium leak is causatively associated with atrial fibrillation in pressure-overloaded hearts 3-Year Outcomes After 2-Stent With Provisional Stenting for Complex Bifurcation Lesions Defined by DEFINITION Criteria TAVI Represents an Anti-Inflammatory Therapy via Reduction of Shear Stress Induced, Piezo-1-Mediated Monocyte Activation
|<<... 170 171 172 173 174 175 176 ...>>|

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