CBS 2019
CBSMD教育中心
English

充血性心力衰竭

科研文章

荐读文献

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure Natriuretic Peptide-Guided Heart Failure Therapy After the GUIDE-IT Study Heart Failure With Mid-Range (Borderline) Ejection Fraction: Clinical Implications and Future Directions The Management of Atrial Fibrillation in Heart Failure: An Expert Panel Consensus Mechanical circulatory support devices for acute right ventricular failure Nocturnal thoracic volume overload and post-discharge outcomes in patients hospitalized for acute heart failure Age-Related Characteristics and Outcomes of Patients With Heart Failure With Preserved Ejection Fraction Guideline‐Directed Medical Therapy for Patients With Heart Failure With Midrange Ejection Fraction: A Patient‐Pooled Analysis From the KorHF and KorAHF Registries Sex- and Race-Related Differences in Characteristics and Outcomes of Hospitalizations for Heart Failure With Preserved Ejection Fraction Sex Differences in Heart Failure With Preserved Ejection Fraction Pathophysiology: A Detailed Invasive Hemodynamic and Echocardiographic Analysis
|<<... 8 9 10 11 12 13 14 ...>>|

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