Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review-Reference-Cited by-同舟云学术

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Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review

Published:2024-06 Issue: Volume:12 Page:102508
ISSN:2215-0161
Container-title:MethodsX
language:en
Short-container-title:MethodsX

Author:

Goh Choon-Hian^ORCID,Ferdowsi Mahbuba^ORCID,Gan Ming Hong,Kwan Ban-Hoe,Lim Wei Yin,Tee Yee Kai,Rosli Roshaslina,Tan Maw Pin

Publisher

Elsevier BV

Subject

Medical Laboratory Technology,Clinical Biochemistry

Reference41 articles.

1. 2018 ESC Guidelines for the diagnosis and management of syncope;Brignole;Eur. Heart J.,2018

2. Clinical Classification of Syncope;Sutton;Prog. Cardiovasc. Dis. [Internet].,2013

3. Syncope: diagnosis and management;Walsh;Curr. Probl. Cardiol.,2015

4. Syncope: epidemiology, etiology, and prognosis;da Silva;Front Physiol,2014

5. Syncope: evaluation and differential diagnosis;Runser;Am. Fam. Physician,2017

Cited by 2 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. Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review;JAMIA Open;2024-04-08

2. Recent Advances and Future Directions in Syncope Management: A Comprehensive Narrative Review;Journal of Clinical Medicine;2024-01-26

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