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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] - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)

  • 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
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    References listed on IDEAS

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    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.
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