IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01578382.html
   My bibliography  Save this paper

Extracting recurrent scenarios from narrative texts using a Bayesian network: Application to serious occupational accidents with movement disturbance

Author

Listed:
  • Fazia Abdat

    (HT - Département Homme au Travail - INRS ( Vandoeuvre lès Nancy) - Institut national de recherche et de sécurité (Vandoeuvre lès Nancy))

  • Sylvie Leclercq

    (HT - Département Homme au Travail - INRS ( Vandoeuvre lès Nancy) - Institut national de recherche et de sécurité (Vandoeuvre lès Nancy))

  • Xavier Cuny

    (CNAM - Conservatoire National des Arts et Métiers [CNAM])

  • Claire Tissot

    (INRS (Paris) - Institut national de recherche et de sécurité (Paris))

Abstract

A probabilistic approach has been developed to extract recurrent serious Occupational Accident with Movement Disturbance (OAMD) scenarios from narrative texts within a prevention framework. Relevant data extracted from 143 accounts was initially coded as logical combinations of generic accident factors. A Bayesian Network (BN)-based model was then built for OAMDs using these data and expert knowledge. A data clustering process was subsequently performed to group the OAMDs into similar classes from generic factor occurrence and pattern standpoints. Finally, the Most Probable Explanation (MPE) was evaluated and identified as the associated recurrent scenario for each class. Using this approach, 8 scenarios were extracted to describe 143 OAMDs in the construction and metallurgy sectors. Their recurrent nature is discussed. Probable generic factor combinations provide a fair representation of particularly serious OAMDs, as described in narrative texts. This work represents a real contribution to raising company awareness of the variety of circumstances, in which these accidents occur, to progressing in the prevention of such accidents and to developing an analysis framework dedicated to this kind of accident.

Suggested Citation

  • Fazia Abdat & Sylvie Leclercq & Xavier Cuny & Claire Tissot, 2014. "Extracting recurrent scenarios from narrative texts using a Bayesian network: Application to serious occupational accidents with movement disturbance," Post-Print hal-01578382, HAL.
    • Handle: RePEc:hal:journl:hal-01578382
    DOI: 10.1016/j.aap.2014.04.004
    Note: View the original document on HAL open archive server: https://hal.science/hal-01578382
    as

    Download full text from publisher

    File URL: https://hal.science/hal-01578382/document
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.aap.2014.04.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    2. Judea Pearl, 2003. "Statistics and causal inference: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(2), pages 281-345, December.
    3. Sylvie Leclercq & S. Thouy & E. Rossignol, 2007. "Progress in understanding processes underlying occupational accidents on the level based on case studies," Post-Print hal-01618321, HAL.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kuroda, Masahiro & Sakakihara, Michio & Geng, Zhi, 2008. "Acceleration of the EM and ECM algorithms using the Aitken [delta]2 method for log-linear models with partially classified data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2332-2338, October.
    2. Chen, Yen-Liang & Hu, Hui-Ling, 2006. "An overlapping cluster algorithm to provide non-exhaustive clustering," European Journal of Operational Research, Elsevier, vol. 173(3), pages 762-780, September.
    3. Wen-Ruey Chang & Sylvie Leclercq & Thurmon E. Lockhart & Roger Haslam, 2016. "State of science: occupational slips, trips and falls on the same level," Post-Print hal-01578740, HAL.
    4. Croft, J. & Smith, J. Q., 2003. "Discrete mixtures in Bayesian networks with hidden variables: a latent time budget example," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 539-547, January.
    5. Sylvie Leclercq, 2014. "Organisational factors of occupational accidents with movement disturbance (OAMD) and prevention," Post-Print hal-01578712, HAL.
    6. Steven M. Shugan, 2007. "—Causality, Unintended Consequences and Deducing Shared Causes," Marketing Science, INFORMS, vol. 26(6), pages 731-741, 11-12.
    7. Esma Nur Cinicioglu & Gül Huyugüzel Kışla & A. Özlem Önder & Y. Gülnur Muradoğlu, 2024. "The Changing Behavior of the European Credit Default Swap Spreads During the Covid-19 Pandemic: A Bayesian Network Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1213-1254, March.
    8. Hongyu Wang & Jian Tang & Pengpeng Xu & Rundong Chen & Haona Yao, 2022. "Research on the Influence Mechanism of Street Vitality in Mountainous Cities Based on a Bayesian Network: A Case Study of the Main Urban Area of Chongqing," Land, MDPI, vol. 11(5), pages 1-22, May.
    9. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2013. "Modeling Differential Item Functioning Using a Generalization of the Multiple-Group Bifactor Model," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 32-60, February.
    10. Federico Castelletti & Alessandro Mascaro, 2021. "Structural learning and estimation of joint causal effects among network-dependent variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1289-1314, December.
    11. Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
    12. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
    13. Sheehan, Barry & Murphy, Finbarr & Mullins, Martin & Ryan, Cian, 2019. "Connected and autonomous vehicles: A cyber-risk classification framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 523-536.
    14. Astrid Kemperman & Pauline van den Berg & Minou Weijs-Perrée & Kevin Uijtdewillegen, 2019. "Loneliness of Older Adults: Social Network and the Living Environment," IJERPH, MDPI, vol. 16(3), pages 1-16, January.
    15. Jie Fan & Baoyin Liu & Xiaodong Ming & Yong Sun & Lianjie Qin, 2022. "The amplification effect of unreasonable human behaviours on natural disasters," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    16. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    17. Kim, Seong-Ho & Kim, Sung-Ho, 2006. "A divide-and-conquer approach in applying EM for large recursive models with incomplete categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 611-641, February.
    18. Rui Han & Shiqi Yang, 2023. "A Study on Industrial Heritage Renewal Strategy Based on Hybrid Bayesian Network," Sustainability, MDPI, vol. 15(13), pages 1-32, July.
    19. Elena Stanghellini & Eduwin Pakpahan, 2015. "Identification of causal effects in linear models: beyond instrumental variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 489-509, September.
    20. Svend Rasmussen & Anders L. Madsen & Mogens Lund, 2013. "Bayesian network as a modelling tool for risk management in agriculture," IFRO Working Paper 2013/12, University of Copenhagen, Department of Food and Resource Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-01578382. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.