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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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Autores principales: Khairuddin, Mohamed Zul Fadhli, Hasikin, Khairunnisa, Abd Razak, Nasrul Anuar, Lai, Khin Wee, Osman, Mohd Zamri, Aslan, Muhammet Fatih, Sabanci, Kadir, Azizan, Muhammad Mokhzaini, Satapathy, Suresh Chandra, Wu, Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9521307/
https://www.ncbi.nlm.nih.gov/pubmed/36187621
http://dx.doi.org/10.3389/fpubh.2022.984099
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author Khairuddin, Mohamed Zul Fadhli
Hasikin, Khairunnisa
Abd Razak, Nasrul Anuar
Lai, Khin Wee
Osman, Mohd Zamri
Aslan, Muhammet Fatih
Sabanci, Kadir
Azizan, Muhammad Mokhzaini
Satapathy, Suresh Chandra
Wu, Xiang
author_facet Khairuddin, Mohamed Zul Fadhli
Hasikin, Khairunnisa
Abd Razak, Nasrul Anuar
Lai, Khin Wee
Osman, Mohd Zamri
Aslan, Muhammet Fatih
Sabanci, Kadir
Azizan, Muhammad Mokhzaini
Satapathy, Suresh Chandra
Wu, Xiang
author_sort Khairuddin, Mohamed Zul Fadhli
collection PubMed
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.
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spelling pubmed-95213072022-09-30 Predicting occupational injury causal factors using text-based analytics: A systematic review Khairuddin, Mohamed Zul Fadhli Hasikin, Khairunnisa Abd Razak, Nasrul Anuar Lai, Khin Wee Osman, Mohd Zamri Aslan, Muhammet Fatih Sabanci, Kadir Azizan, Muhammad Mokhzaini Satapathy, Suresh Chandra Wu, Xiang Front Public Health Public Health 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 /pmc/articles/PMC9521307/ /pubmed/36187621 http://dx.doi.org/10.3389/fpubh.2022.984099 Text en Copyright © 2022 Khairuddin, Hasikin, Abd Razak, Lai, Osman, Aslan, Sabanci, Azizan, Satapathy and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Khairuddin, Mohamed Zul Fadhli
Hasikin, Khairunnisa
Abd Razak, Nasrul Anuar
Lai, Khin Wee
Osman, Mohd Zamri
Aslan, Muhammet Fatih
Sabanci, Kadir
Azizan, Muhammad Mokhzaini
Satapathy, Suresh Chandra
Wu, Xiang
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 Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9521307/
https://www.ncbi.nlm.nih.gov/pubmed/36187621
http://dx.doi.org/10.3389/fpubh.2022.984099
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