Document Type : Articles

Authors

1 Universiti Teknologi MARA

2 Universitas Majalengka

Abstract

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.

Keywords

Main Subjects

Abd Aziz, F. S., Abdullah, K. H. & Samsudin, S. (2021). Bibliometric analysis of behavior-based safety (BBS): Three decades publication trends. Webology18, 278-293. https://doi.org/10.14704/web/v18si02/web18072
Abdel-Aal, R. E. & Mangoud, A. M. (1996). Abductive machine learning for modeling and predicting the educational score in school health surveys. Methods of Information in Medicine, 35(03), 265-271. https://doi.org/10.1055/s-0038-1634655
Abdullah, K. H. & Abd Aziz, F. S. (2020). Safety behaviour in the Laboratory among university students. The Journal of Behavioral Science15(3), 51-65. Retrieved from https://so06.tci-thaijo.org/index.php/IJBS/article/view/241208
Abdullah, K. H., Hashim, M. N. & Abd Aziz, F. S. (2020). A 39 years (1980-2019) bibliometric analysis of safety leadership research. TEST Engineering and Management, 83, 4526-4542. 
Ashok, M., Madan, R., Joha, A. & Sivarajah, U. (2022). Ethical framework for Artificial Intelligence and Digital technologies. International Journal of Information Management, 62, 1-17. https://doi.org/10.1016/j.ijinfomgt.2021.102433
Badri, A., Boudreau-Trudel, B. & Souissi, A. S. (2018). Occupational health and safety in the industry 4.0 era: A cause for major concern?. Safety Science, 109, 403-411. https://doi.org/10.1016/j.ssci.2018.06.012
Bas, G. & Koseoglu, M. A. (2019). Analysis of the warehouse work accidents in logistics sector. Press Academia Procedia, 9(1), 262-268. https://doi.org/10.17261/Pressacademia.2019.1102
Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?. The Journal of Arthroplasty, 33(8), 2358-2361. https://doi.org/10.1016/j.arth.2018.02.067
Bonifazi, G., Corradini, E., Ursino, D., Virgili, L., Anceschi, E. & De Donato, M. C. (2021). A machine learning based sentient multimedia framework to increase safety at work. Multimedia Tools and Applications, 81 (1), 141-169. https://doi.org/10.1007/s11042-021-10984-z
Dhawan, S. M., Gupta, B. M. & Singh, N. K. (2020). Global Machine-learning Research: a scientometric assessment of global literature during 2009–18. World Digital Libraries-An International Journal, 13(2), 105-120. https://doi.org/10.18329/09757597/2020/13209
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
Duan, Z. & Zhang, K. (2006). Data mining technology for structural health monitoring. Pacific Science Review, 8(1), 27-36.
El Naqa, I. & Murphy, M. J. (2015). What is machine learning?. In machine learning in radiation oncology (pp. 3-11). Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_1
Gianfrancesco, M. A., Tamang, S., Yazdany, J. & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine, 178(11), 1544-1547. https://doi.org/10.1001/jamainternmed.2018.3763
Goh, Y. C., Cai, X. Q., Theseira, W., Ko, G. & Khor, K. A. (2020). Evaluating human versus machine learning performance in classifying research abstracts. Scientometrics, 125(2), 1197-1212. https://doi.org/10.1007/s11192-020-03614-2
Gordan, M., Ismail, Z. B., Razak, H. A., Ghaedi, K. & Ghayeb, H. H. (2020). Optimisation-based evolutionary data mining techniques for structural health monitoring. Journal of Civil Engineering and Construction, 9(1), 14-23. https://doi.org/10.32732/jcec
Gordan, M., Sabbagh-Yazdi, S. R., Ismail, Z., Ghaedi, K., Carroll, P., McCrum, D. & Samali, B. (2022). State-of-the-art review on advancements of data mining in structural health monitoring. Measurement, 193, 1-38. https://doi.org/10.1016/j.measurement.2022.110939
Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23(1), 40-55. https://doi.org/10.1038/s41580-021-00407-0
Gul, M. & Ak, M. F. (2018). A comparative outline for quantifying risk ratings in occupational health and safety risk assessment. Journal of Cleaner Production, 196, 653-664. https://doi.org/10.1016/j.jclepro.2018.06.106
Hamet, P. & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, 36-40. https://doi.org/10.1016/j.metabol.2017.01.011
Heo, Seongbong, Moonil Kim, Hangnan Yu, Woo-Kyun Lee, Jong Ryeul Sohn, Soon-Young Jung, Kyong Whan Moon  &Sang Hoon Byeon (2018). Chemical accident hazard assessment by spatial analysis of chemical factories and accident records in South Korea. International Journal of Disaster Risk Reduction, 27, 37-47. https://doi.org/10.1016/j.ijdrr.2017.09.016
Huang VS, Morris K, Jain M, Ramesh BM, Kemp H, Blanchard J, Isac S, Sarkar B, Gothalwal V, Namasivayam V &  Kumar P. (2020). Closing the gap on institutional delivery in northern India: A case study of how integrated machine learning approaches can enable precision public health. BMJ Global Health, 5(10), 1-14. http://dx.doi.org/10.1136/bmjgh-2020-002340
Jayatilake, S. M. D. A. C. & Ganegoda, G. U. (2021). Involvement of machine learning tools in healthcare decision making. Journal of Healthcare Engineering, 2021, 1-20. https://doi.org/10.1155/2021/6679512
Jha, I. P., Awasthi, R., Kumar, A., Kumar, V. & Sethi, T. (2021). Learning the mental health impact of covid-19 in the united states with explainable artificial intelligence: Observational study. JMIR Mental Health, 8(4), 1-11. https://doi.org/10.2196/25097
Jiao, Z., Hu, P., Xu, H. & Wang, Q. (2020). Machine learning and deep learning in chemical health and safety: a systematic review of techniques and applications. ACS Chemical Health & Safety, 27(6), 316-334. https://dx.doi.org/10.1021/acs.chas.0c00075
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
Koklonis, K., Sarafidis, M., Vastardi, M. & Koutsouris, D. (2021). Utilisation of machine learning in supporting occupational safety and health decisions in hospital workplace. Engineering, Technology & Applied Science Research, 11(3), 7262-7272. http://dx.doi.org/10.48084/etasr.4205
Krstić, B., Rađenović, T. & Živković, S. (2022). Occupational Health and Safety Performance Management System: Conceptual Framework, Design, and Implementation in an Enterprise. In Handbook of Research on Key Dimensions of Occupational Safety and Health Protection Management (pp. 1-26). IGI Global. https://doi.org/10.4018/978-1-7998-8189-6.ch001
Lalmuanawma, S., Hussain, J. & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139, 110059. https://doi.org/10.1016/j.chaos.2020.110059
Liakos, K. G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 1-29. https://doi.org/10.3390/s18082674
Maheronnaghsh, S., Zolfagharnasab, H., Gorgich, M. & Duarte, J. (2021). Machine learning in occupational safety and health: protocol for a systematic review. International Journal of Occupational and Environmental Safety, 5(1), 32-38. https://doi.org/10.24840/2184-0954_005.001_0004
Marcus, J. L., Hurley, L. B., Krakower, D. S., Alexeeff, S., Silverberg, M. J. & Volk, J. E. (2019). Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study. The Lancet HIV, 6(10), 688-695. https://doi.org/10.1016/S2352-3018(19)30137-7
Martinez, S., del Mar Delgado, M., Marin, R. M. & Alvarez, S. (2019). Science mapping on the Environmental Footprint: A scientometric analysis-based review. Ecological Indicators, 106, 1-11. https://doi.org/10.1016/j.ecolind.2019.105543
Mingers, J. & Leydesdorff, L. (2015). A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1), 1-19. https://doi.org/10.1016/j.ejor.2015.04.002
Mukhamediev, R. I., Symagulov, A., Kuchin, Y., Yakunin, K. & Yelis, M. (2021). From classical machine learning to deep neural networks: A simplified scientometric review. Applied Sciences, 11(12), 1-26. https://doi.org/10.3390/app11125541
Naeem M, Jamal T, Diaz-Martinez J, Butt SA, Montesano N, Tariq MI, De-la-Hoz-Franco E & De-La-Hoz-Valdiris E.  (2022). Trends and future perspective challenges in big data. In Advances in Intelligent Data Analysis and Applications (pp. 309-325). Springer, Singapore. https://doi.org/10.1007/978-981-16-5036-9_30
Niu, X. S. (2014). International scientific collaboration between Australia and China: A mixed-methodology for investigating the social processes and its implications for national innovation systems. Technological Forecasting and Social Change, 85, 58-68. https://doi.org/10.1016/j.techfore.2013.10.014
Paltrinieri, N., Comfort, L. & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475-486. https://doi.org/10.1016/j.ssci.2019.06.001
Poh, C. Q., Ubeynarayana, C. U. & Goh, Y. M. (2018). Safety leading indicators for construction sites: A machine learning approach. Automation in Construction, 93, 375-386. https://doi.org/10.1016/j.autcon.2018.03.022
Ruiz-Rosero, J., Ramírez-González, G. & Viveros-Delgado, J. (2019). Software survey: ScientoPy, a scientometric tool for topics trend analysis in scientific publications. Scientometrics, 121(2), 1165-1188. https://doi.org/10.1007/s11192-019-03213-w
Sachs, L. (1990). Safety and risk in a context of culture. International Journal of Risk & Safety in Medicine, 1(4), 255-265. https://doi.org/10.3233/jrs-1990-1402
Sarkar, S. & Maiti, J. (2020). Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis. Safety Science, 131, 1-25. https://doi.org/10.1016/j.ssci.2020.104900
Shao, B., Hu, Z., Liu, Q., Chen, S. & He, W. (2019). Fatal accident patterns of building construction activities in China. Safety Science, 111, 253-263. https://doi.org/10.1016/j.ssci.2018.07.019
Siegel, E. (2019, February 2). Five Ways Your Safety Depends on Machine Learning. KDnuggets. Retrieved from https://www.kdnuggets.com/2019/02/dr-data-five-ways-safety-depends-machine-learning.html
Simsekler, M. C. E., Rodrigues, C., Qazi, A., Ellahham, S. & Ozonoff, A. (2021). A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms. Reliability Engineering & System Safety, 208, 1-13. https://doi.org/10.1016/j.ress.2020.107416
Sirola, M. & Hulsund, J. E. (2021). Machine-Learning Methods in Prognosis of Ageing Phenomena in Nuclear Power Plant Components. International Scientific Journal of Computing, 20(1), 11-21. https://doi.org/10.47839/ijc.20.1.2086
Sofyan, D. & Abdullah, K. H. (2022). Scientific developments in educational innovation research in Indonesia and Malaysia: a scientometric review. International Journal of Educational Innovation and Research, 1(1), 42-51. https://doi.org/10.31949/ijeir.v1i1.2312
Stahl BC, Andreou A, Brey P, Hatzakis T, Kirichenko A, Macnish K, Shaelou SL, Patel A, Ryan M & Wright D.  (2021). Artificial intelligence for human flourishing–Beyond principles for machine learning. Journal of Business Research, 124, 374-388. https://doi.org/10.1016/j.jbusres.2020.11.030
Surya, L. (2016). An exploratory study of Machine Learning and it’s future in the United States. International Journal of Creative Research Thoughts (IJCRT), 4(1), 862-866.
Tang, S. & Golparvar-Fard, M. (2021). Machine learning-based risk analysis for construction worker safety from ubiquitous site photos and videos. Journal of Computing in Civil Engineering, 35(6), 1-19. https://doi.org/10.1061/(asce)cp.1943-5487.0000979
Trung, N. D., Huy, D. T. N. & Le, T. H. (2021). IoTs, machine learning (ML), AI and digital transformation affects various industries-principles and cybersecurity risks solutions.  Webology, 18, 501-513. https://doi.org/10.14704/web/v18si04/web18144
Tullu, M. S. (2019). Writing the title and abstract for a research paper: Being concise, precise, and meticulous is the key. Saudi Journal of Anaesthesia, 13(Suppl 1), 12-17. https://doi.org/10.4103/sja.sja_685_18
Van Nunen, K., Li, J., Reniers, G. & Ponnet, K. (2018). Bibliometric analysis of safety culture research. Safety Science, 108, 248-258. https://doi.org/10.1016/j.ssci.2017.08.011
Van, T. N. & Quoc, T. N. (2021). Research Trends on Machine Learning in Construction Management: A Scientometric Analysis. Journal of Applied Science and Technology Trends, 2(03), 96-104. https://doi.org/10.38094/jastt203105
Vardakas, K. Z., Tsopanakis, G., Poulopoulou, A. & Falagas, M. E. (2015). An analysis of factors contributing to PubMed’s growth. Journal of Informetrics, 9(3), 592-617. https://doi.org/10.1016/j.joi.2015.06.001
Varshney, K. R. (2016, January). Engineering safety in machine learning. In 2016 Information Theory and Applications Workshop (ITA) (pp. 1-5). IEEE. https://doi.org/10.1109/ita.2016.7888195
Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, Jung K, Heller K, Kale D, Saeed M & Ossorio PN (2019). Do no harm: a roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337-1340. https://doi.org/10.1038/s41591-019-0548-6
Woolley, R., Turpin, T., Marceau, J. & Hill, S. (2008). Mobility matters: Research training and network building in science. Comparative Technology Transfer and Society, 6(3), 159-184. https://doi.org/10.1353/ctt.0.0014
Yang, W., Fidelis, T. T. & Sun, W. H. (2019). Machine learning in catalysis, from proposal to practicing. ACS omega, 5(1), 83-88. https://doi.org/10.1021/acsomega.9b03673
Yong, Z., Xiaoming, Z. & Alshehri, M. D. (2021). A machine learning-enabled intelligent application for public health and safety. Neural Computing and Applications, 1-14. https://doi.org/10.1007/s00521-021-06301-2
Zhu, R., Hu, X., Hou, J. & Li, X. (2021). Application of machine learning techniques for predicting the consequences of construction accidents in China. Process Safety and Environmental Protection, 145, 293-302. https://doi.org/10.1016/j.psep.2020.08.006
Zhu, Y., Kim, D., Yan, E., Kim, M. C. & Qi, G. (2021). Analysing China’s research collaboration with the United States in high-impact and high-technology research. Quantitative Science Studies, 2(1), 363-375. https://doi.org/10.1162/qss_a_00098