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Predicting Occupational Injury Causal Factors Using Text-based Analytics: A Systematic Review

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dc.contributor.authorMohamed Zul Fadhli Khairuddinen_US
dc.contributor.authorKhairunnisa Hasikinen_US
dc.contributor.authorNasrul Anuar Abd Razaken_US
dc.contributor.authorKhin Wee Laien_US
dc.contributor.authorMohd Zamri Osmanen_US
dc.contributor.authorMuhammet Fatih Aslanen_US
dc.contributor.authorKadir Sabancien_US
dc.contributor.authorMuhammad Mokhzaini Azizanen_US
dc.contributor.authorSuresh Chandra Satapathyen_US
dc.contributor.authorXiang Wuen_US
dc.date.accessioned2024-05-28T05:49:04Z
dc.date.available2024-05-28T05:49:04Z
dc.date.issued2022
dc.date.submitted2022-11-11
dc.description.abstractWorkplace 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.en_US
dc.identifier.citationKhairuddin MZF, Hasikin K, Abd Razak NA, Lai KW, Osman MZ, Aslan MF, Sabanci K, Azizan MM, Satapathy SC and Wu X (2022) Predicting occupational injury causal factors using text-based analytics: A systematic review. Front. Public Health 10:984099. doi: 10.3389/fpubh.2022.984099en_US
dc.identifier.doi10.3389/fpubh.2022.984099
dc.identifier.epage17
dc.identifier.issn2296-2565
dc.identifier.issue2022
dc.identifier.other2494-20
dc.identifier.spage1
dc.identifier.urihttps://www.frontiersin.org/articles/10.3389/fpubh.2022.984099/full
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/6587
dc.identifier.volume2022
dc.language.isoenen_US
dc.publisherFRONTIERSen_US
dc.relation.ispartofFrontiers in Public Healthen_US
dc.subjectnatural language processing, artificial intelligence, machine learning, deep learning, occupational health and safetyen_US
dc.titlePredicting Occupational Injury Causal Factors Using Text-based Analytics: A Systematic Reviewen_US
dc.typeArticleen_US
dspace.entity.typePublication

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