Predicting occupational injury causal factors using text-based analytics: A systematic review.

Autor:	Khairuddin MZF; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.; Institute of Medical Science Technology, Universiti Kuala Lumpur, Selangor, Malaysia., Hasikin K; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.; Centre of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia., Abd Razak NA; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia., Lai KW; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia., Osman MZ; Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Gambang, Malaysia., Aslan MF; Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey., Sabanci K; Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey., Azizan MM; Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia., Satapathy SC; School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India., Wu X; School of Medical Information and Engineering, Xuzhou Medical University Xuzhou, Xuzhou, Jiangsu, China.
Jazyk:	angličtina
Zdroj:	Frontiers in public health [Front Public Health] 2022 Sep 15; Vol. 10, pp. 984099. Date of Electronic Publication: 2022 Sep 15 (Print Publication: 2022).
DOI:	10.3389/fpubh.2022.984099
Abstrakt:	Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Khairuddin, Hasikin, Abd Razak, Lai, Osman, Aslan, Sabanci, Azizan, Satapathy and Wu.)
Databáze:	MEDLINE
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