Khairuddin, Mohamed Zul Fadhli and Hasikin, Khairunnisa and Abd Razak, Nasrul Anuar and Lai, Khin Wee and Osman, Mohd Zamri and Aslan, Muhammet Fatih and Sabanci, Kadir and Azizan, Muhammad Mokhzaini and Satapathy, Suresh Chandra and Wu, Xiang (2022) Predicting occupational injury causal factors using text-based analytics: A systematic review. Frontiers in Public Health, 10. ISSN 2296-2565, DOI https://doi.org/10.3389/fpubh.2022.984099.
Full text not available from this repository.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, Naive 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 |
|---|---|
| Funders: | Xuzhou Science and Technology Project [KC21182] |
| Uncontrolled Keywords: | Natural language processing; Artificial intelligence; Machine learning; Deep learning; Occupational health and safety |
| Subjects: | R Medicine T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Aug 2023 07:52 |
| Last Modified: | 30 Aug 2023 07:52 |
| URI: | http://eprints.um.edu.my/id/eprint/41096 |
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