Modeling Bayesian Networks from a conceptual framework for occupational risk analysis
Vieira, Elamara Marama de Araujo; Silva, Jonhatan Magno Norte da; Silva, Luiz Bueno da
Abstract
Occupational risk is the possibility that some element included in a particular work environment can cause damage to someone’s health. Thereby, the risk is understood as the product of probability and consequences. In this sense, risk analysis through probabilistic stochastic techniques, such as Bayesian networks (BN), becomes an important tool to analyze occupational risks. Thus, this article aims to show how BNs are being used in the field of occupational risk analysis, and to develop a conceptual framework for the construction of the BNs. Therefore, a systematic review analogous to the Statement for Reporting Systematic Reviews (PRISMA) protocol was performed, which allowed the evaluation of learning methods with the BN, building models and also for us propose a conceptual framework for the implementation of BNs in the analysis of occupational risks.
Keywords
References
Abdat, F., Leclercq, S., Cuny, X., & Tissot, C. (2014). Extracting recurrent scenarios from narrative texts using a Bayesian network: application to serious occupational accidents with movement disturbance. Accident: Analysis and Prevention, 70, 155-166. PMid:24769246. http://dx.doi.org/10.1016/j.aap.2014.04.004.
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., & Salmerón, A. (2011). Bayesian networks in environmental modelling. Environmental Modelling & Software, 26(12), 1376-1388. http://dx.doi.org/10.1016/j.envsoft.2011.06.004.
Akhtar, M. J., & Utne, I. B. (2014). Human fatigue’s effect on the risk of maritime groundings: a Bayesian Network modeling approach. Safety Science, 62, 427-440. http://dx.doi.org/10.1016/j.ssci.2013.10.002.
Aneziris, O. N., Papazoglou, I. A., & Doudakmani, O. (2010). Assessment of occupational risks in an aluminium processing industry. International Journal of Industrial Ergonomics, 40(3), 321-329. http://dx.doi.org/10.1016/j.ergon.2010.01.005.
Areosa, J. (2012). The importance of workers risk perceptions. International Journal on Working Conditions, 3, 54-64.
Arlinghaus, A., Lombardi, D. A., Willetts, J. L., Folkard, S., & Christiani, D. C. (2012). A structural equation modeling approach to fatigue-related risk factors for occupational injury. American Journal of Epidemiology, 176(7), 597-607. PMid:22956514. http://dx.doi.org/10.1093/aje/kws219.
Ben-Gal, I., Shani, A., Gohr, A., Grau, J., Arviv, S., Shmilovici, A., Posch, S., & Grosse, I. (2005). Identification of transcription factor binding sites with variable-order Bayesian networks. Bioinformatics, 21(11), 2657-2666. PMid:15797905. http://dx.doi.org/10.1093/bioinformatics/bti410.
Carvalho, J. V. F., & Chiann, C. (2013). Redes Bayesianas: um método para avaliação de interdependência e contágio em séries temporais multivariadas. Revista Brasileira de Economia, 67(2), 201-217. http://dx.doi.org/10.1590/S0034-71402013000200003.
Chatterjee, S. (2014). Development of uncertainty-based work injury model using Bayesian structural equation modelling. International Journal of Injury Control and Safety Promotion, 21(4), 318-327. PMid:24111548. http://dx.doi.org/10.1080/17457300.2013.825629.
Chen, B., Wang, J., & Chen, S. (2009). Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. International Journal of Advanced Manufacturing Technology, 48(1-4), 83-94. http://dx.doi.org/10.1007/s00170-009-2258-6.
Chen, S. H., & Pollino, C. A. (2012). Good practice in Bayesian network modelling. Environmental Modelling & Software, 37(1), 134-145. http://dx.doi.org/10.1016/j.envsoft.2012.03.012.
Delcroix, V., Sedki, K., & Lepoutre, F. (2013). A Bayesian network for recurrent multi-criteria and multi-attribute decision problems: choosing a manual wheelchair. Expert Systems with Applications, 40(7), 2541-2551. http://dx.doi.org/10.1016/j.eswa.2012.10.065.
Englehardt, J. D., An, H., Fleming, L. E., & Bean, J. A. (2003). Analytical predictive bayesian assessment of occupational injury risk: municipal solid waste collectors. Risk Analysis, 23(5), 917-927. PMid:12969407. http://dx.doi.org/10.1111/1539-6924.00369.
Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2-3), 131-163. http://dx.doi.org/10.1023/A:1007465528199.
García-Herrero, S., Mariscal, M. A., Ritzel, D. O., & García-Rodríguez, J. (2012a). Working conditions, psychological/physical symptoms and occupational accidents: Bayesian network models. Safety Science, 50(9), 1760-1774. http://dx.doi.org/10.1016/j.ssci.2012.04.005.
García-Herrero, S., Saldaña, M. Á. M., Rodriguez, J. G., & Ritzel, D. O. (2012b). Influence of task demands on occupational stress: gender differences. Journal of Safety Research, 43(5-6), 365-374. PMid:23206509. http://dx.doi.org/10.1016/j.jsr.2012.10.005.
García-Herrero, S., Mariscal, M. A., Gutiérrez, J. M., & Ritzel, D. O. (2013a). Using Bayesian networks to analyze occupational stress caused by work demands: preventing stress through social support. Accident; Analysis and Prevention, 57, 114-123. PMid:23672926. http://dx.doi.org/10.1016/j.aap.X2013.04.009.
García-Herrero, S., Mariscal, M. A., Gutiérrez, J. M., & Toca-Otero, A. (2013b). Bayesian network analysis of safety culture and organizational culture in a nuclear power plant. Safety Science, 53, 82-95. http://dx.doi.org/10.1016/j.ssci.2012.09.004.
Griffiths, T. L., & Yuille, A. (2006). Technical Introduction: a primer on probabilistic inference. Trends in Cognitive Sciences, 10(7), 327-334.
Guerrero-Barbosa, T. E., & Amarís-Castro, G. E. (2014). Application of Bayesian techniques for the identification of accident-prone road sections. Dyna, 81(187), 209-214. http://dx.doi.org/10.15446/dyna.v81n187.41333.
Hamra, G. B., Loomis, D., & Dement, J. (2014). Examining the association of lung cancer and highly correlated fibre size-specific asbestos exposures with a hierarchical Bayesian model. Occupational and Environmental Medicine, 71(5), 353-357. PMid:24569623. http://dx.doi.org/10.1136/oemed-2013-101965.
Hänninen, M., Valdez Banda, O. A., & Kujala, P. (2014). Bayesian network model of maritime safety management. Expert Systems with Applications, 41(17), 7837-7846. http://dx.doi.org/10.1016/j.eswa.2014.06.029.
Hsu, H.-I., Lin, M.-Y., Chen, Y.-C., Chen, W.-Y., Yoon, C., Chen, M.-R., & Tsai, P.-J. (2014). An integrated approach to assess exposure and health-risk from polycyclic aromatic hydrocarbons (PAHs) in a fastener manufacturing industry. International Journal of Environmental Research and Public Health, 11(9), 9578-9594. PMid:25226413. http://dx.doi.org/10.3390/ijerph110909578.
Jordan, M. I. (1999). Learning in graphical models. Cambridge: MIT Press.
Khakzad, N., Khan, F., & Amyotte, P. (2013a). Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Safety and Environmental Protection, 91(1-2), 46-53. http://dx.doi.org/10.1016/j.psep.2012.01.005.
Khakzad, N., Khan, F., & Amyotte, P. (2013b). Quantitative risk analysis of offshore drilling operations: A Bayesian approach. Safety Science, 57, 108-117. http://dx.doi.org/10.1016/j.ssci.2013.01.022.
Lee, E. G., Kim, S. W., Feigley, C. E., & Harper, M. (2013). Exposure models for the prior distribution in bayesian decision analysis for occupational hygiene decision making. Journal of Occupational and Environmental Hygiene, 10(2), 97-108. PMid:23252451. http://dx.doi.org/10.1080/15459624.2012.748627.
Leitão, I., Fernandes, A. L., & Ramos, I. C. (2008). Saúde ocupacional: analisando os riscos relacionados à equipe. Ciência, Cuidado e Saúde, 7(4), 476-484.
Leu, S.-S., & Chang, C.-M. (2013). Bayesian-network-based safety risk assessment for steel construction projects. Accident: Analysis and Prevention, 54, 122-133. PMid:23499984. http://dx.doi.org/10.1016/j.aap.2013.02.019.
Liao, C.-W. (2012). Pattern analysis of seasonal variation in occupational accidents in the construction industry. Procedia Engineering, 29, 3240-3244. http://dx.doi.org/10.1016/j.proeng.2012.01.473.
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 339(j1), b2700. PMid:19622552. http://dx.doi.org/10.1136/bmj.b2700.
Martín, J. E., Rivas, T., Matías, J. M., Taboada, J., & Argüelles, A. (2009). A Bayesian network analysis of workplace accidents caused by falls from a height. Safety Science, 47(2), 206-214. http://dx.doi.org/10.1016/j.ssci.2008.03.004.
Martins, M. R., & Maturana, M. C. (2013). Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents. Reliability Engineering & System Safety, 110, 89-109. http://dx.doi.org/10.1016/j.ress.2012.09.008.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Francisco: Morgan Kaufmann.
Pearl, J., & Russel, S. (2001). Bayesian networks. In M. Arbib (Ed.), Handbook of brain theory and neural networks (2nd ed.). Cambridge, MIT Press. p. 157-160.
Puncher, M., Birchall, A., Bull, R. K. (2013). A Bayesian analysis of uncertainties on lung doses resulting from occupational exposures to uranium. Radiation Protection Dosimetry, 156(2), 131-140. PMid:23528329. http://dx.doi.org/10.1093/rpd/nct062.
Ren, J., Jenkinson, I., Wang, J., Xu, D. L., & Yang, J. B. (2008). A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors. Journal of Safety Research, 39(1), 87-100. PMid:18325420. http://dx.doi.org/10.1016/j.jsr.2007.09.009.
Rivas, T., Paz, M., Martín, J. E., Matías, J. M., García, J. F., & Taboada, J. (2011). Explaining and predicting workplace accidents using data-mining techniques. Reliability Engineering & System Safety, 96(7), 739-747. http://dx.doi.org/10.1016/j.ress.2011.03.006.
Schenekenberg, N. C. M., Malucelli, A., Dias, J. S., & Cubas, M. R. (2011). Redes bayesianas para eleição da ventilação mecânica no pós-operatório de cirurgia cardíaca. Fisioterapia em Movimento, 24(3), 481-492. http://dx.doi.org/10.1590/S0103-51502011000300013.
Taylor, J. A., Lacovara, A. V., Smith, G. S., Pandian, R., & Lehto, M. (2014). Near-miss narratives from the fire service: a Bayesian analysis. Accident; Analysis and Prevention, 62, 119-129. PMid:24144497. http://dx.doi.org/10.1016/j.aap.2013.09.012.
Tighe, M., Pollino, C. A., & Wilson, S. C. (2013). Bayesian Networks as a screening tool for exposure assessment. Journal of Environmental Management, 123, 68-76. PMid:23583867. http://dx.doi.org/10.1016/j.jenvman.2013.03.018.
Valdés, R. M. A., Comendador, F. G., Gordún, L. M., & Sáez Nieto, F. J. (2011). The development of probabilistic models to estimate accident risk (due to runway overrun and landing undershoot) applicable to the design and construction of runway safety areas. Safety Science, 49(5), 633-650. http://dx.doi.org/10.1016/j.ssci.2010.09.020.
Wang, Y. F., Faghih Roohi, S., Hu, X. M., & Xie, M. (2011). Investigations of Human and Organizational Factors in hazardous vapor accidents. Journal of Hazardous Materials, 191(1-3), 69-82. PMid:21571433. http://dx.doi.org/10.1016/j.jhazmat.2011.04.040.
Wang, Y. F., Xie, M., Chin, K.-S., & Fu, X. J. (2013). Accident analysis model based on Bayesian Network and Evidential Reasoning approach. Journal of Loss Prevention in the Process Industries, 26(1), 10-21. http://dx.doi.org/10.1016/j.jlp.2012.08.001.
Wang, Z., Hope, R. M., Wang, Z., Ji, Q., & Gray, W. D. (2012). Cross-subject workload classification with a hierarchical Bayes model. NeuroImage, 59(1), 64-69. PMid:21867763. http://dx.doi.org/10.1016/j.neuroimage.2011.07.094.
Weber, P., Medina-Oliva, G., Simon, C., & Iung, B. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25(4), 671-682. http://dx.doi.org/10.1016/j.engappai.2010.06.002.
Xing, L., Burstyn, I., Richardson, D. B., & Gustafson, P. (2013). A comparison of Bayesian hierarchical modeling with group-based exposure assessment in occupational epidemiology. Statistics in Medicine, 32(21), 3686-3699. PMid:23553785. http://dx.doi.org/10.1002/sim.5791.
Zhao, L., Wang, X., & Qian, Y. (2012). Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: a case study in China. Safety Science, 50(4), 1049-1055. http://dx.doi.org/10.1016/j.ssci.2011.12.003.
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email