Occupational health and safety and data mining: a bibliometric analysis
DOI:
https://doi.org/10.15675/gepros.v16i2.2784Palavras-chave:
Análise de bibliometria, Saúde e Segurança no Trabalho, Mineração de dadosResumo
Purpose - This article aims to carry out a bibliometric analysis on data mining and occupational health and safety, covering the period between 2008 and 2020, for seven scientific databases and 68 articles.
Theoretical framework - This study was theoretically based on concepts that involve data mining, machine learning and occupational health and safety.
Design/methodology/approach - The selected articles were submitted to a statistical analysis, together with the evaluation of one of the bibliometric laws (Bradford's Law), comprising a number of citations, journals, authors, countries of origin, publication categories and an evaluation of production over the years.
Findings - As a result, it was found that the most influential journal was Safety Science, and Taiwan was the leading country in terms of articles produced, with an average of 115 citations per article. The best-ranked journals related to Engineering and Health, both corresponding to 30% of the selected articles and journals.
Originality/value - This study provides some insights into the growth of the data mining area together with occupational health and safety.
Keywords - Bibliometrics analysis. Occupational health and safety. Data mining.
Referências
ARAÚJO, C. A. Bibliometria: evolução histórica e questões atuais. Em Questão, v. 12, n. 1, p. 11-32, 2006.
ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 14280: Cadastro de acidente do trabalho - Procedimento e classificação. Rio de Janeiro, p. 1-94, 2001.
AZIZ, S. F. A.; OSMAN, F. Does compulsory training improve occupational safety and health implementation? The case of Malaysian. Safety Science, v. 111, p. 205–212, 2019. DOI: https://doi.org/10.1016/j.ssci.2018.07.012
AZZOLIN, K.; SOUZA, E. N.; RUSCHEL, K. B.; MUSSI, C. M.; LUCENA, A. F.; RABELO, E. R. Consenso de diagnósticos, resultados e intervenções de enfermagem para pacientes com insuficiência cardíaca em domicílio. Revista Gaúcha de Enfermagem, v. 33, n. 4, p. 56–63, 2012. DOI: https://doi.org/10.1590/S1983-14472012000400007
BADRI, A.; BOUDREAU-TRUDEL, B.; SOUISSI, A. S. Occupational health and safety in the industry 4.0 era: A cause for major concern?. Safety Science, v. 109, p. 403-411, 2018. DOI: https://doi.org/10.1016/j.ssci.2018.06.012
BATISTA, A. G.; SANTANA, V. S.; FERRITE, S. Registro de dados sobre acidentes de trabalho fatais em sistemas de informação no Brasil. Revista Ciência e Saúde Coletiva, v.24, p. 693-704, 2019. DOI: https://doi.org/10.1590/1413-81232018243.35132016
BUCZAK, A. L., GUVEN, E. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Commuications Surveys & Tutorials v. 18, n. 2, p. 1153–1176, 2016. DOI: https://doi.org/10.1109/COMST.2015.2494502
CHEN, H.; HOU, C.; ZHANG, L.; LI, S. Comparative study on the strands of research on the governance model of international occupational safety and health issues. Safety Science, v. 122, p. 104513, 2020. DOI: https://doi.org/10.1016/j.ssci.2019.104513
CHENG, C. W.; LIN, C. C.; LEU, S. SEN. Use of association rules to explore cause-effect relationships in occupational accidents in the Taiwan construction industry. Safety Science, v. 48, n. 4, p. 436–444, 2010. DOI: https://doi.org/10.1016/j.ssci.2009.12.005
CHENG, C. W.; YAO, H. Q.; WU, T. C. Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry. Journal of Loss Prevention in the Process Industries, v. 26, n. 6, p. 1269–1278, 2013. DOI: https://doi.org/10.1016/j.jlp.2013.07.002
CHENG, C.-W., LEU, S. S., CHENG, Y. M., WU, T. C., LIN, C. C. Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan’s construction industry. Accident Analysis & Prevention, v. 48, p. 214–222, 2012. DOI: https://doi.org/10.1016/j.aap.2011.04.014
CHOI, J., GU, B., CHIN, S., LEE, J. S. Machine learning predictive model based on national data for fatal accidents of construction workers. Automation in Construction, v. 110, p. 102974, 2020. DOI: https://doi.org/10.1016/j.autcon.2019.102974
CIARAPICA, F. E.; GIACCHETTA, G. Classification and prediction of occupational injury risk using soft computing techniques: An Italian study. Safety Science, v. 47, n. 1, p. 36–49, 2009. DOI: https://doi.org/10.1016/j.ssci.2008.01.006
COLNAGO, L; SIVOLELLA, R. Convenção 187 da OIT: promoção da saúde e segurança do trabalho no Brasil e a viabilidade de sua ratificação. Revista eletrônica do Tribunal Regional do Trabalho da 9ª Região, v. 8, p. 144-156, 2019.
COMBERTI, L.; DEMICHELA, M.; BALDISSONE, G. A combined approach for the analysis of large occupational accident databases to support accident-prevention decision making. Safety Science, v. 106, p. 191–202, 2018. DOI: https://doi.org/10.1016/j.ssci.2018.03.014
DEL POZO-ANTÚNEZ, J. J., ARIZA-MONTES, A. FERNÁNDEZ-NAVARRO, F. MOLINA-SÁNCHEZ, H. Effect of a job demand-control-social support model on accounting professionals’ health perception. International Journal of Environmental Research and Public Health, v. 15, n. 11, 2018. DOI: https://doi.org/10.3390/ijerph15112437
FAYYAD, U.; PIATETSKY-SHAPIRO, G.; SMYTH, P. From Data Mining to Knowledge Discovery in databases. AI Magazine. v. 17, n. 3, p. 37–54, 1996.
HAJAKBARI, M. S.; MINAEI-BIDGOLI, B. A new scoring system for assessing the risk of occupational accidents: A case study using data mining techniques with Iran’s Ministry of Labor data. Journal of Loss Prevention in the Process Industries, v. 32, p. 443–453, 2014. DOI: https://doi.org/10.1016/j.jlp.2014.10.013
HAN, J; KAMBER, M.; PEI, J. Data Mining Concepts and Techniques. 3 ed. Waltham: Morgan Kaufmann Publishers, 2012.
LIAO, C.-W.; PERNG, Y.-H. Data mining for occupational injuries in the Taiwan construction industry. Safety Science, v. 46, n. 7, p. 1091–1102, 2008. DOI: https://doi.org/10.1016/j.ssci.2007.04.007
MUTLU, N. G.; ALTUNTAS, S. Risk analysis for occupational safety and health in the textile industry: Integration of FMEA, FTA, and BIFPET methods. International Journal of Industrial Ergonomics, v. 72, p. 222-240, 2019. DOI: https://doi.org/10.1016/j.ergon.2019.05.013
PALAMARA, F.; PIGLIONE, F.; PICCININI, N. Self-Organizing Map and clustering algorithms for the analysis of occupational accident databases. Safety Science, v. 49, n. 8–9, p. 1215–1230, 2011. DOI: https://doi.org/10.1016/j.ssci.2011.04.003
PIMENTA, A. A., PORTELA, A. R. M. R., OLIVEIRA, C. B. O. RIBEIRO, R. M. A Bibliometria as Pesquisas Acadêmicas. Revista de Ensino, Pesquisa e Extensão, v. 4, n. 7, 2017.
QUEVEDO-SILVA, F., SANTOS, E. B. A., BRANDÃO, M. M., VILS, L. Estudo Bibliométrico: Orientações Sobre Sua Aplicação. Revista Brasileira De Marketing, v. 15, n. 2, p. 246-262, 2016. DOI: https://doi.org/10.5585/remark.v15i2.3274
RUSO, J.; STOJANOVIĆ, V. Occupational health and safety using data mining. International Journal for Quality Research, v. 6, n. 4, 2012.
SÁNCHEZ-HERRERA, I. S.; DONATE, M. J. Occupational safety and health (OSH) and business strategy: The role of the OSH professional in Spain. Safety Science, v. 120, p. 206–225, 2019. DOI: https://doi.org/10.1016/j.ssci.2019.06.037
SANMIQUEL, L.; ROSSELL, J. M.; VINTRÓ, C. Study of Spanish mining accidents using data mining techniques. Safety Science, v. 75, p. 49–55, 2015. DOI: https://doi.org/10.1016/j.ssci.2015.01.016
SHIN, D.-P., YOUNG-JUN, P., SEO, J., DONG-EUN, L. Association Rules Mined from Construction Accident Data. KSCE Journal of Civil Engineering, v. 22, n. 4, p. 1027-1039, 2018. DOI: https://doi.org/10.1007/s12205-017-0537-6
TIXIER, A. J.-P., HALLOWELL, M. R., RAJAGOPALAN, B. BOWMAN, D. Construction Safety Clash Detection: Identifying Safety Incompatibilities among Fundamental Attributes using Data Mining. Automation in Construction, v. 74, p. 39, 2017. DOI: https://doi.org/10.1016/j.autcon.2016.11.001
WANG, Y.; CHEN, H; LIU, B; YANG, M; LONG, Q. A Systematic Review on the Research Progress and Evolving Trends of Occupational Health and Safety Management: A Bibliometric Analysis of Mapping Knowledge Domains. Frontiers in Public Health, v. 8, 2020. DOI: https://doi.org/10.3389/fpubh.2020.00081
WITTEN, I., FRANK, E. Data Mining: Practical Machine Learning Tools and Techniques. 4 ed. São Francisco: Morgan Kaufmann, 2016.
YANAR, B.; LAY, M.; SMITH, P. M. The Interplay Between Supervisor Safety Support and Occupational Health and Safety Vulnerability on Work Injury. Safety and Health at Work, v. 10, n. 2, p. 172–179, 2019. DOI: https://doi.org/10.1016/j.shaw.2018.11.001
YILMAZA, F., ÇELEBIB, U. B. The Importance of Safety in Construction Sector: Costs of Occupational Accidents in Construction Sites. Business and Economics Research Journal, v. 6, n. 2, p. 25-37. 2015.
ZHANG, D; JIANG, K. Application of Data Mining Techniques in the Analysis of Fire Incidents. Procedia Engineering, v.43, p. 250–256, 2012. DOI: https://doi.org/10.1016/j.proeng.2012.08.043
ZHAO, Y., ZHANG, C., ZHANG, Y., WANG, Z., LI, J. Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise. Ear and Hearing, v. 40, n. 3, p. 690–699, 2019. DOI: https://doi.org/10.1097/AUD.0000000000000649
Downloads
Arquivos adicionais
Publicado
Como Citar
Edição
Seção
Licença
O(s) autor(es) do artigo autorizam a publicação do texto na revista e garantem que a contribuição é original e inédita, não estando em processo de avaliação em outra(s) revista(s). As opiniões, ideias e conceitos emitidos nos textos são de inteira responsabilidade do(s) autor(es), não sendo a revista responsável por tais conteúdos.
Os editores da revista reservam o direito de efetuar ajustes textuais e de adequação às normas da publicação, caso necessário.
Os autores mantêm os direitos autorais sobre o trabalho e concedem à revista o direito de primeira publicação, sendo o trabalho simultaneamente licenciado sob a Attribution 4.0 International (CC BY 4.0), o que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.
Os autores têm autorização para firmar contratos adicionais, separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista.
