Advertisement

Acquisition of Adaptation Knowledge for Breast Cancer Treatment Decision Support

  • Jean Lieber
  • Mathieu d’Aquin
  • Pierre Bey
  • Amedeo Napoli
  • Maria Rios
  • Catherine Sauvagnac
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

The elaboration of a treatment in cancerology depends on decision protocols. These protocols are often adapted rather than used straightforwardly. This paper deals with the acquisition of the knowledge exploited during protocol adaptations. It shows that this knowledge acquisition process can be based on similarity paths, that are used for representing the matchings between decision problems (e.g., source and target problems within a case-based reasoning process).

Keywords

Knowledge Acquisition Breast Cancer Treatment Virtual Patient Target Problem Similarity Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dubois, D., Prade, H., Sabbadin, R.: Decision-theoretic foundations of qualitative possibility theory. European Journal of Operational Research 128, 459–478 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Evidence-based medicine working-group. Evidence-based medicine. A new approach to teaching the practice of medicine. JAMA, 17, p. 268 (1992)Google Scholar
  3. 3.
    Fuchs, B., Lieber, J., Mille, A., Napoli, A.: An Algorithm for Adaptation in Case-Based Reasoning. In: Proceedings of the 14th European Conference on Artificial Intelligence (ECAI-2000), Berlin, Germany, pp. 45–49 (2000)Google Scholar
  4. 4.
    Fuchs, B., Mille, A.: A Knowledge-Level Task Model of Adaptation in Case-Based Reasoning. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS (LNAI), vol. 1650, pp. 118–131. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  5. 5.
    Hanney, K., Keane, M.T.: Learning Adaptation Rules From a Case-Base. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 179–192. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  6. 6.
    Jarmulak, J., Craw, S., Rowe, R.: Using Case-Base Data to Learn Adaptation Knowledge for Design. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI 2001), pp. 1011–1016. Morgan Kaufmann, Inc., San Francisco (2001)Google Scholar
  7. 7.
    Lieber, J.: Strong, Fuzzy and Smooth Hierarchical Classification for Case-Based Problem Solving. In: van Harmelen, F. (ed.) Proceedings of the 15th European Conference on Artificial Intelligence (ECAI 2002), Lyon, France, pp. 81–85. IOS Press, Amsterdam (2002)Google Scholar
  8. 8.
    Lieber, J., Bresson, B.: Case-Based Reasoning for Breast Cancer Treatment Decision Helping. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 173–185. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Lieber, J., d’Aquin, M., Bey, P., Bresson, B., Croissant, O., Falzon, P., Lesur, A., Lévĕque, J., Mollo, V., Napoli, A., Rios, M., Sauvagnac, C.: The Kasimir Project: Knowledge Management in Cancerology. In: Proc. of the 4th International Workshop on Enterprise Networking and Computing in Health Care Industry (HealthComm 2002), pp. 125–127 (2002)Google Scholar
  10. 10.
    Lieber, J., d’Aquin, M., Bey, P., Napoli, A., Rios, M., Sauvagnac, C.: Adaptation Knowledge Acquisition, a Study for Breast Cancer Treatment. In: Research report available on LORIA (January 2003), http://www.loria.fr/equipes/orpailleur/
  11. 11.
    Lieber, J., Napoli, A.: Using Classification in Case-Based Planning. In: Wahlster, W. (ed.) Proceedings of the 12th European Conference on Artificial Intelligence (ECAI 1996), Budapest, Hungary, pp. 132–136. John Wiley & Sons, Ltd., Chichester (1996)Google Scholar
  12. 12.
    Melis, E., Lieber, J., Napoli, A.: Reformulation in Case-Based Reasoning. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 172–183. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. 13.
    Napoli, A., Laurenço, C., Ducournau, R.: An object-based representation system for organic synthesis planning. Int. Journal of Human-Computer Studies 41(1/2), 5–32 (1994)CrossRefGoogle Scholar
  14. 14.
    Oliver, D.E., Shahar, Y., Musen, M.A., Shortliffe, E.H.: Representation of Change in Controlled Medical Terminologies. Artificial Intelligence in Medicine 15(1), 53–76 (1999)CrossRefGoogle Scholar
  15. 15.
    Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates, Hillsdale (1989)Google Scholar
  16. 16.
    Sauvagnac, C.: La construction de connaissances par l’utilisation et la conception de procédures. Contribution au cadre théorique des activités métafonctionnelles. Thèse d’Université, Conservatoire National des Arts et Métiers (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jean Lieber
    • 1
  • Mathieu d’Aquin
    • 1
  • Pierre Bey
    • 2
    • 3
  • Amedeo Napoli
    • 1
  • Maria Rios
    • 4
  • Catherine Sauvagnac
    • 5
  1. 1.Orpailleur research group, LORIACNRS, INRIA, Nancy UniversitiesVandœuvre-lès-Nancy
  2. 2.Réseau OncolorVandœuvre-lès-NancyFrance
  3. 3.Institut CurieParisFrance
  4. 4.Centre Alexis VautrinServices d’OncologieVandœuvre-lès-Nancy
  5. 5.Laboratoire d’ergonomie du CNAMParis

Personalised recommendations