Mohamed Zul Fadhli, Khairuddin and Khairunnisa, Hasikin and Nasrul Anuar, Abd Razak and Lai, Khin Wee and Mohd Zamri, Osman and Aslan, Muhammet Fatih and Sabanci, Kadir and Muhammad Mokhzaini, Azizan and Satapathy, Suresh Chandra (2022) Predicting occupational injury causal factors using text-based analytics : A systematic review. Frontiers in Public Health, 10 (984099). pp. 1-17. ISSN 2296-2565. (Published)
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Abstract
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.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Artificial intelligence; Deep learning; Machine learning; Natural language processing; Occupational health and safety |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 11 Apr 2023 06:47 |
| Last Modified: | 11 Apr 2023 06:47 |
| URI: | http://umpir.ump.edu.my/id/eprint/37433 |
| Download Statistic: | View Download Statistics |
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