Semantic Representation of Evidence-Based Clinical Guidelines

  • Zhisheng HuangEmail author
  • Annette ten TeijeEmail author
  • Frank van Harmelen
  • Salah Aït-Mokhtar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8903)


Evidence-based Clinical Guidelines (EbCGs) are document or recommendation which have been created using the best clinical research findings of the highest value to aid in the delivery of optimum clinical care to patients. In this paper, we propose a lightweight formalism of evidence-based clinical guidelines by introducing the Semantic Web Technology for it. With the help of the tools which have been developed in the Semantic Web and Natural Language Processing (NLP), the generation of the formulations of evidence-based clinical guidelines become much easy. We will discuss several usecases of the semantic representation of EbCGs, and argue that it is potentially useful for the applications of the semantic web technology on the medical domain.



This work is partially supported by the European Commission under the 7th framework programme EURECA Project (FP7-ICT-2011-7, Grant 288048).


  1. 1.
    Aït-Mokhtar, S., Bruijn, B.D., Hagège, C., Rupi, P.: Initial prototype for relation identification between concepts, D3.2. Technical report, EURECA Project (2013)Google Scholar
  2. 2.
    Aït-Mokhtar, S., Chanod, J.-P., Roux, C.: Robustness beyond shallowness: incremental deep parsing. Nat. Lang. Eng. 8(2), 121–144 (2002)Google Scholar
  3. 3.
    Claerhout, B., De Schepper K., et al.: Initial EURECA architecture, D2.2. Technical report, EURECA Project (2013)Google Scholar
  4. 4.
    de Clercq, P., Blom, J., Korsten, H., Hasman, A.: Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif. Intell. Med. 31, 1–27 (2004)CrossRefGoogle Scholar
  5. 5.
    Fensel, D., van Harmelen, F., Andersson, B., Brennan, P., Cunningham, H., Della Valle, E., Fischer, F., Huang, Z., Kiryakov, A., Lee, T., School, L., Tresp, V., Wesner, S., Witbrock, M., Zhong, N.: Towards LarKC: a platform for web-scale reasoning. In: Proceedings of the IEEE International Conference on Semantic Computing (ICSC2008). IEEE Computer Society Press, CA, USA (2008)Google Scholar
  6. 6.
    Fox, J., Johns, N., Lyons, C., Rahmanzadeh, A., Thomson, R., Wilson, P.: Proforma: a general technology for clinical decision support systems. Comput. Methods Programs Biomed. 54, 59–67 (1997)CrossRefGoogle Scholar
  7. 7.
    Fox, J., Johns, N., Rahmanzadeh, A., Thomson, R.: Proforma, approaches for creating computer-interpretable guidelines that facilitate decision support. In: Proceedings of Medical Informatics Europe, Amsterdam (1996)Google Scholar
  8. 8.
    Huang, Z., ten Teije, A., van Harmelen, F.: SemanticCT: a semantically-enabled system for clinical trials. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., ten Teije, A. (eds.) KGC 2013 and ProHealth 2013. LNCS, vol. 8268, pp. 11–25. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Karras, B., Nath, S., Shiffman, R.: A preliminary evaluation of guideline content markup using GEM - an XML guideline elements model. In: Proceedings of AMIA Annual Symposium (2000)Google Scholar
  10. 10.
    Lindberg, D., Humphreys, B., McCray, A.: The unified medical language system. Methods Inf. Med. 32(4), 281–291 (1993)Google Scholar
  11. 11.
    NABON.: Breast cancer, dutch guideline, version 2.0. Technical report, Integraal kankercentrum Netherland, Nationaal Borstkanker Overleg Nederland (2012)Google Scholar
  12. 12.
    Peleg, M., Boxwala, A., Ogunyemi, O., et al.: GLIF3: The evolution of a guideline representation format. In: Proceedings of AMIA Annual Fall Symposium, pp. 645–649 (2000)Google Scholar
  13. 13.
    Peleg, M., Boxwala, A., Tu, S., Ogunyemi, O., Zeng, Q., Wang, D.: Guideline interchange format 3.4. Technical report (2001)Google Scholar
  14. 14.
    Shahar, Y., Miksch, S., Johnson, P.: The asgaard project: a task specific framework for the application and critiquing of time oriented clinical guidelines. Artif. Intell. Med. 14, 29–51 (1998)CrossRefGoogle Scholar
  15. 15.
    Shahar, Y., Young, O., Shalom, E., Mayaffit, A., Moskovitch, R., Hessing, A., Galperin, M.: DEGEL: a hybrid, multiple-ontology framework for specification and retrieval of clinical guidelines. In: Dojat, M., Keravnou, E., Barahona, P. (eds.) AIME 2003. LNCS (LNAI), vol. 2780, pp. 122–131. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    Tu, S., Musen, M.: A flexible approach to guideline modeling. In: Proceeding of 1999 AMIA Symposium, pp. 420–424 (1999)Google Scholar
  17. 17.
    Witbrock, M., Fortuna, B., Bradesko, L., Kerrigan, M., Bishop, B., van Harmelen, F., ten Teije, A., Oren, E., Momtchev, V., Tenschert, A., Cheptsov, A., Roller, S., Gallizo, G.: D5.3.1 - requirements analysis and report on lessons learned during prototyping. LarKC project deliverable, June 2009Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceVU University AmsterdamAmsterdamThe Netherlands
  2. 2.Xerox Research Centre EuropeMeylanFrance

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