CBS 2019
CBSMD教育中心
English

充血性心力衰竭

科研文章

荐读文献

Universal Definition and Classification of Heart Failure: A Report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure Heart Failure Outcomes With Volume-Guided Management The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure Is Cardiac Diastolic Dysfunction a Part of Post-Menopausal Syndrome? A Fully Magnetically Levitated Circulatory Pump for Advanced Heart Failure Heart Failure With Improved Ejection Fraction-Is it Possible to Escape One’s Past? How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC) Heart Failure With Mid-Range (Borderline) Ejection Fraction: Clinical Implications and Future Directions Association Between Functional Impairment and Medication Burden in Adults with Heart Failure Prdm16 Deficiency Leads to Age-Dependent Cardiac Hypertrophy, Adverse Remodeling, Mitochondrial Dysfunction, and Heart Failure
|<<... 7 8 9 10 11 12 13 ...>>|

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