Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review-Reference-Cited by-同舟云学术

Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review

Published:2020-07-27 Issue:1 Volume:26 Page:23-34
ISSN:1382-4147
Container-title:Heart Failure Reviews
language:en
Short-container-title:Heart Fail Rev

Author:

Bazoukis George^ORCID,Stavrakis Stavros,Zhou Jiandong,Bollepalli Sandeep Chandra,Tse Gary,Zhang Qingpeng,Singh Jagmeet P.,Armoundas Antonis A.

Funder

American Heart Association

RICBAC foundation

Publisher

Springer Science and Business Media LLC

Subject

Cardiology and Cardiovascular Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10741-020-10007-3.pdf

Reference33 articles.

1. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Colvin MM et al (2017) 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation 136:e137–ee61

2. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS et al (2016) 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J 37:2129–2200

3. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS et al (2016) 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Rev Esp Cardiol (Engl Ed) 69:1167

4. Ponikowski P, Anker SD, AlHabib KF, Cowie MR, Force TL, Hu S et al (2014) Heart failure: preventing disease and death worldwide. ESC Heart Failure 1:4–25

5. Writing Group M, Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ et al (2016) Heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circulation 133:e38–e360

Cited by 66 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Refining heart disease prediction accuracy using hybrid machine learning techniques with novel metaheuristic algorithms;International Journal of Cardiology;2024-12

2. A Review on Machine Learning Applications: CVI Risk Assessment;Tehnicki vjesnik - Technical Gazette;2024-08-15

3. Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study;JMIR Cardio;2024-07-23

4. The Utility of Artificial Intelligence and Machine Learning in the Diagnosis of Takotsubo Cardiomyopathy: A Systematic Review;Heart and Mind;2024-07

5. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications;Canadian Journal of Cardiology;2024-07