Predicting occupational injury causal factors using text-based analytics : A systematic review
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. Th...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37433/1/Predicting%20occupational%20injury%20causal%20factors%20using%20text-based%20analytics_A%20systematic%20review.pdf |
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| author | Mohamed Zul Fadhli, Khairuddin Khairunnisa, Hasikin Nasrul Anuar, Abd Razak Lai, Khin Wee Mohd Zamri, Osman Aslan, Muhammet Fatih Sabanci, Kadir Muhammad Mokhzaini, Azizan Satapathy, Suresh Chandra |
| author_facet | Mohamed Zul Fadhli, Khairuddin Khairunnisa, Hasikin Nasrul Anuar, Abd Razak Lai, Khin Wee Mohd Zamri, Osman Aslan, Muhammet Fatih Sabanci, Kadir Muhammad Mokhzaini, Azizan Satapathy, Suresh Chandra |
| author_sort | Mohamed Zul Fadhli, Khairuddin |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T13:05:53Z |
| format | Article |
| id | UMPir37433 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:05:53Z |
| publishDate | 2022 |
| publisher | Frontiers Media S.A. |
| record_format | dspace |
| spelling | UMPir374332023-04-11T06:47:58Z http://umpir.ump.edu.my/id/eprint/37433/ Predicting occupational injury causal factors using text-based analytics : A systematic review Mohamed Zul Fadhli, Khairuddin Khairunnisa, Hasikin Nasrul Anuar, Abd Razak Lai, Khin Wee Mohd Zamri, Osman Aslan, Muhammet Fatih Sabanci, Kadir Muhammad Mokhzaini, Azizan Satapathy, Suresh Chandra QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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. Frontiers Media S.A. 2022-09-15 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37433/1/Predicting%20occupational%20injury%20causal%20factors%20using%20text-based%20analytics_A%20systematic%20review.pdf 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) https://doi.org/10.3389/fpubh.2022.984099 https://doi.org/10.3389/fpubh.2022.984099 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Mohamed Zul Fadhli, Khairuddin Khairunnisa, Hasikin Nasrul Anuar, Abd Razak Lai, Khin Wee Mohd Zamri, Osman Aslan, Muhammet Fatih Sabanci, Kadir Muhammad Mokhzaini, Azizan Satapathy, Suresh Chandra Predicting occupational injury causal factors using text-based analytics : A systematic review |
| title | Predicting occupational injury causal factors using text-based analytics : A systematic review |
| title_full | Predicting occupational injury causal factors using text-based analytics : A systematic review |
| title_fullStr | Predicting occupational injury causal factors using text-based analytics : A systematic review |
| title_full_unstemmed | Predicting occupational injury causal factors using text-based analytics : A systematic review |
| title_short | Predicting occupational injury causal factors using text-based analytics : A systematic review |
| title_sort | predicting occupational injury causal factors using text based analytics a systematic review |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/37433/1/Predicting%20occupational%20injury%20causal%20factors%20using%20text-based%20analytics_A%20systematic%20review.pdf |
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