CBS 2019
CBSMD教育中心
English

充血性心力衰竭

科研文章

荐读文献

Heart Failure With Mid-Range (Borderline) Ejection Fraction: Clinical Implications and Future Directions Empagliflozin Increases Cardiac Energy Production in Diabetes - Novel Translational Insights Into the Heart Failure Benefits of SGLT2 Inhibitors Is Cardiac Diastolic Dysfunction a Part of Post-Menopausal Syndrome? The year in cardiology: heart failure: The year in cardiology 2019 Association of Abnormal Left Ventricular Functional Reserve With Outcome in Heart Failure With Preserved Ejection Fraction Frequency, predictors, and prognosis of ejection fraction improvement in heart failure: an echocardiogram-based registry study Percutaneous Atriotomy for Levoatrial–to–Coronary Sinus Shunting in Symptomatic Heart Failure: First-in-Human Experience Mechanical circulatory support devices for acute right ventricular failure Age-Related Characteristics and Outcomes of Patients With Heart Failure With Preserved Ejection Fraction Baseline Features of the VICTORIA (Vericiguat Global Study in Subjects With Heart Failure With Reduced Ejection Fraction) Trial
|<<... 5 6 7 8 9 10 11 ...>>|

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