Machine Learning Application: A Bibliometric Analysis From a Half-Century of Research on Stroke.

Autor:	Che Nawi CMNH; Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, MYS., Mohd Hairon S; Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, MYS., Wan Yahya WNN; Department of Internal Medicine/ Neurology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, MYS., Wan Zaidi WA; Department of Internal Medicine/ Neurology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, MYS., Hassan MR; Department of Community Health, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, MYS., Musa KI; Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, MYS.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2023 Aug 26; Vol. 15 (8), pp. e44142. Date of Electronic Publication: 2023 Aug 26 (Print Publication: 2023).
DOI:	10.7759/cureus.44142
Abstrakt:	The quick advancement of digital technology through artificial intelligence has made it possible to deploy machine learning to predict stroke outcomes. Our aim is to examine the trend of machine learning applications in stroke-related research over the past 50 years. We used search terms stroke and machine learning to search for English versions of original and review articles and conference proceedings published over the past 50 years in Scopus and Web of Science databases. The Biblioshiny web application was utilized for the analysis. The trend of publication and prominent authors and journals were analyzed and identified. The collaborative network between countries was mapped, and a thematic map was used to monitor the authors' trending keywords. In total, 10,535 publications authored by 44,990 authors from 2,212 sources were retrieved. Two distinct clusters of collaborative network nodes were observed, with the United States serving as a connecting node. Three terms - deep learning, algorithms, and neural networks - are observed in the early stages of the emerging theme. Overall, international research collaborations, the establishment of global research initiatives, the development of computational science, and the availability of big data have facilitated the pervasive use of machine learning techniques in stroke research. Competing Interests: The authors have declared that no competing interests exist. (Copyright © 2023, Che Nawi et al.)
Databáze:	MEDLINE
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