Machine Learning in Safety and Health Research: A Scientometric Analysis

• Published in: International Journal of Information Science and Management
• Main Author: Abdullah K.H.; Sofyan D.
• Format: Article
• Language: English
• Published: Regional Inform. Center for Sci. and Technol. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146668545&doi=10.22034%2fijism.2022.1977763.0&partnerID=40&md5=74a1e39bc533a47d353142abd01b92d7

Safety and health are intricately interwoven and have become indispensable to the thriving business world and anthropology. It is concerned with ensuring employees’ physical, emotional, and mental well-being. Based on the Scopus and Web of Science databases, the current study intends to analyse the global research output on machine learning in safety and health. This study utilized ScientoPy and VOSviewer to delve into the annual growth, patterns of research communication on source titles, international collaboration among countries, and authors’ keyword analysis. This study found that the Web of Science database tracks the evolution of publications throughout time. PLoS One has surpassed all other source titles in terms of publishing activity. Also, this study indicated that US researchers are constantly working on machine learning in safety and health research and have developed significant collaborations with China and Australia. Between 2020 and 2021, the University of Toronto published 86% of all papers, outpacing other institutions. The keywords “machine learning”, “artificial intelligence”, “electronic health records”, “deep learning”, and “mental health” were the most popular and trending keywords in 2020 and 2021, and “artificial intelligence” appeared in most publications among others. Future researchers should conduct scoping or systematic literature reviews to elucidate the relationships between these terms. This study may entice the curiosity of practitioners and researchers to advance new knowledge in this field by being devoted to cutting-edge research in the contemporary philosophy of science, cognitive, and cultural anthropology on machine learning in safety and health research. In conclusion, this scientometric analysis demonstrates that machine learning in safety and health is a study domain that requires further refinement in future research, as this technology has the potential to significantly improve workplace safety and health through targeted applications with clear benefits © 2023,International Journal of Information Science and Management.All Rights Reserved.