Analysing Clinical Guidelines’ Contents with Deontic and Rhetorical Structures

  • Gersende Georg
  • Hugo Hernault
  • Marc Cavazza
  • Helmut Prendinger
  • Mitsuru Ishizuka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)


The computerisation of clinical guidelines can greatly benefit from the automatic analysis of their content using Natural Language Processing techniques. Because of the central role played by specific deontic structures, known as recommendations, it is possible to tune the processing step towards the recognition of such expressions, which can be used to structure key sections of the document. In this paper, we extend previous work on the automatic identification of guidelines’ recommendations, by showing how Rhetorical Structure Theory can be used to characterise the actual contents of elementary recommendations. The emphasis on causality and time in RST proves a powerful complement to the recognition of deontic structures and supports the identification of relevant knowledge, in particular for the identification of conditional structures, which play an important role for the subsequent analysis of recommendations.


natural language processing rhetorical structure theory guidelines 


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  1. 1.
    Shiffman, R., Karras, B., Agrawal, A., Chen, R., Marenco, L., Nath, S.: GEM: A proposal for a more comprehensive guideline document model using XML. J. Am. Med. Informatics Assoc. 7, 488–498 (2000)CrossRefGoogle Scholar
  2. 2.
    Shahar, Y., Young, O., Shalom, E., Mayaffit, A., Moskovitch, R., Hessing, A., Galperin, M.: The Digital electronic Guideline Library (DeGeL): a hybrid framework for representation and use of clinical guidelines. Stud. Health Technol. Inform. 101, 147–151 (2004)PubMedGoogle Scholar
  3. 3.
    Georg, G., Jaulent, M.-C.: A Document Engineering Environment for Clinical Guidelines. In: King, P.R., Simske, S.J. (eds.) Proceedings of the 2007 ACM Symposium on Document Engineering, pp. 69–78. ACM Press, New York (2007)CrossRefGoogle Scholar
  4. 4.
    Fuchs, N.E., Kaljurand, K., Schneider, G.: Attempto Controlled English Meets the Challenges of Knowledge Representation, Reasoning, Interoperability and User Interfaces. In: FLAIRS Conference, pp. 664–669 (2006)Google Scholar
  5. 5.
    Georg, G., Jaulent, M.-C.: An Environment for Document Engineering of Clinical Guidelines. In: Proceedings AMIA Symposium, pp. 276–280 (2005)Google Scholar
  6. 6.
    Georg, G., Cavazza, M.: Integrating Document-based and Knowledge-based Models for Clinical Guideline Analysis. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 421–430. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: Toward a functional theory of text organisation. Text 8(3), 243–281 (1988)Google Scholar
  8. 8.
    Taboada, M., Mann, W.C.: Applications of Rhetorical Structure Theory. Discourse Studies 8(4), 567–588 (2006)CrossRefGoogle Scholar
  9. 9.
    Piwek, P., Hernault, H., Prendinger, H., Ishizuka, M.: T2D: Generating Dialogues Between Virtual Agents Automatically from Text. Intelligent Virtual Agents, 161–174 (2007)Google Scholar
  10. 10.
    Grasso, F.: Rhetorical coding of health promotion dialogues. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds.) AIME 2003. LNCS, vol. 2780, pp. 179–188. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Carlson, L., Marcu, D., Okurowski, M.E.: Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory. In: Proc. of the Second SIGdial Workshop on Discourse and Dialogue. Annual Meeting of the ACL, vol. 16, pp. 1–10 (2001)Google Scholar
  12. 12.
    Soricut, R., Marcu, D.: Sentence level discourse parsing using syntactic and lexical information. In: Proc. of the 2003 Conference of the North American Chapter of the Association For Computational Linguistics on Human Language Technology, vol. 1, pp. 149–156 (2003)Google Scholar
  13. 13.
    Gallardo, S.: Pragmatic support of medical recommendations in popularized texts. Journal of Pragmatics 37(6), 813–835 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gersende Georg
    • 1
  • Hugo Hernault
    • 2
  • Marc Cavazza
    • 3
  • Helmut Prendinger
    • 4
  • Mitsuru Ishizuka
    • 2
  1. 1.Haute Autorité de SantéSaint-Denis La PlaineFrance
  2. 2.Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
  3. 3.School of ComputingUniversity of TeessideMiddlesbroughUnited Kingdom
  4. 4.National Institute of InformaticsTokyoJapan

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