Abstract
Treatment of women in occupational health research needs a new perspective to reduce gender gaps. In this study, machine learning through Bayesian modeling is applied to a large dataset with 172,026 records from medical examinations carried out to workers from different companies engaged in several strategic economic sectors in Spain. The Bayesian models generated together with the application of recent information theory parameters are expected to unveil hidden health risk factors that must be considered in the design of future medical protocols and policies. Finally, the aim of this study is also to promote the introduction of AI solutions in future gender studies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
United Nations: Transforming our world: the 2030 Agenda for Sustainable Development. In: Resolution adopted by the General Assembly on 25 September 2015, A/RES/70/1
Hagberg, M., Punnett, L., Bergqvist, U., Burdorf, A., Hrenstam, A., Kristensen, T.S., Lillienberg, L., Quinn, M., Smith, T.J., Westberg H.: Broadening the view of exposure assessment. Scand. J. Work Environ. Health. 27(5), 354–357 (2001)
Leach, A.: 12 steps to achieve gender equality in our lifetimes. In: The Guardian, International Edition: Working in development, 14 Mar 2016
Messing, A., Punnett, A., Bond, M., Alexanderson, K., Pyle, J., Zahm, S., Wegman, D., Stock, S.R., Grosbois, S.: Be the fairest of them all: challenges and recommendations for the treatment of gender in occupational health research. Am. J. Ind. Med. 43, 618–629 (2003)
Bernard, B., Sauter, S., Fine, L., Petersen, M., Hales, P.: Title of paper if known. Scand. J. Work Environ. Health 20(6), 417–426 (1994)
Wijnhoven, H., de Vet, H., Picavet, H.: Prevalence of musculoskeletal disorders is systematically higher in women than in men. Clin. J. Pain 22(8), 717–724 (2006)
Perles-Riber, J., Rodríguez, A., Sevilla, M., Moreno, L.: Unemployment effects of economic crises on hotel and residential tourism destinations: the case of Spain. Tourism Manage. 54, 356–368 (2016)
Zhu, Y., Jankay, R., Pieratt, L., Mehta, R.: Wearable sensors and their metrics for measuring comprehensive occupational fatigue: a scoping review. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 61(1), 1041–1045 (2017)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers, INC (2014)
Bayesia, S.A.S.: BayesiaLab 7, Bayesian networks for research and analytics (2018). http://www.bayesia.com/
Vergara, J., Estévez, P.: A review of feature selection methods based on mutual information. Neural Comput. Appl. 24(1), 175–186 (2014)
Conrady, S., Jouffe, L.: Bayesian networks & BayesiaLab. A practical introduction for researches. Bayesia USA, Franklin, TN (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gerassis, S., Abad, A., Saavedra, Á., García, J.F., Taboada, J. (2019). Women’s Occupational Health: Improving Medical Protocols with Artificial Intelligence Solutions. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_88
Download citation
DOI: https://doi.org/10.1007/978-3-030-01057-7_88
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01056-0
Online ISBN: 978-3-030-01057-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)