Skip to main content

Advertisement

Springer Nature Link
Log in

Modeling adaptation of breast cancer treatment decision protocols in the Kasimir project

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Medical decision protocols constitute theories for health-care decision making that are applicable for “standard” medical cases but have to be adapted for the other cases. This holds in particular for the breast cancer treatment protocol studied in the Kasimir research project. Protocol adaptations can be seen as knowledge-intensive case-based decision support processes. Some examples of adaptations that have been performed by oncologists are presented in this paper. Several issues are then identified that need to be addressed while trying to model such processes, namely: the complexity of adaptations, the lack of relevant information about the patient, the necessity to take into account the applicability and the consequences of a decision, the closeness to decision thresholds, and the necessity to consider some patients according to different viewpoints. As handling these issues requires some additional knowledge, which has to be acquired, different methods are presented that perform adaptation knowledge acquisition either from experts, or in a semi-automatic manner. A discussion and a conclusion end the paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Peleg M, Tu S, Bury J, Ciccarese P, Fox J, Greenes RA, Hall R, Johnson PD, Jones N, Kumar A, Miksch S, Quaglini S, Seyfang A, Shortliffe EH, Stefanelli M (2003) Comparing computer-interpretable guideline models: a case-study approach. J Am Med Inform Assoc 10(1):52–68

    Article  Google Scholar 

  2. Kaiser K, Miksch S, Tu SW (eds) (2004) Proceedings of the symposium on computerized guidelines and protocols (CGP-2004), Studies in health technology and informatics, vol 101. IOS, Amsterdam

    Google Scholar 

  3. Sauvagnac C (2000) 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

  4. Riesbeck CK, Schank RC (1989) Inside case-based reasoning. Lawrence Erlbaum Associates, Hillsdale

    Google Scholar 

  5. Aamodt A (1990) Knowledge-intensive case-based reasoning and sustained learning. In: Aiello LC (ed) Proceedings of the 9th European conference on artificial intelligence (ECAI’90)

  6. Evidence-based medicine working-group. Evidence-based medicine (1992) A new approach to teaching the practice of medicine. J Am Med Assoc 17:268

    Google Scholar 

  7. McCarthy J (1977) Epistemological problems of artificial intelligence. In: Proceedings of the 5th international joint conference on artificial intelligence (IJCAI’77), Cambridge, MA, pp 1038–1044

  8. d’Aquin M, Brachais S, Lieber J, Napoli A (2004) Decision support and knowledge management in oncology using hierarchical classification. In: Kaiser K, Miksch S, Tu SW (eds) Proceedings of the symposium on computerized guidelines and protocols (CGP-2004). Studies in health technology and informatics, vol 101. IOS, Amsterdam, pp 16–30

    Google Scholar 

  9. Bechhofer S, van Harmelen F, Hendler J, Horrocks I, McGuinness DL, Patel-Schneider PF, Stein LA (2006) OWL web ontology language reference. www.w3.org/TR/owl-ref. Last consultation: October 2006

  10. Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P (eds) (2003) The description logic handbook. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  11. d’Aquin M, Lieber J, Napoli A (2005) Decentralized case-based reasoning for the semantic web. In: Gil Y, Motta E (eds) Proceedings of the 4th international semantic web conference (ISWC 2005). Lecture notes in computer science, vol 3729. Springer, Berlin, pp 142–155

    Google Scholar 

  12. Antoniou G, van Harmelen F (2005) A semantic web primer. MIT, Cambridge

    Google Scholar 

  13. Maximini K, Maximini R, Bergmann R (2003) An investigation of generalized cases. In: Ashley KD, Bridge D (eds) Proceedings of the 5th international conference on case base reasoning (ICCBR’03), Trondheim, Norway. Lecture notes in artificial intelligence, vol 2689. Springer, Berlin, pp 261–275

    Google Scholar 

  14. Lieber J, Napoli A (1996) Using classification in case-based planning. In: Wahlster W (ed) Proceedings of the 12th European conference on artificial intelligence (ECAI’96), Budapest, Hungary. Wiley, New York, pp 132–136

    Google Scholar 

  15. Lieber J, Napoli A (1998) Correct and complete retrieval for case-based problem-solving. In: Prade H (ed) Proceedings of the 13th European conference on artificial intelligence (ECAI-98), Brighton, United Kingdom, pp 68–72

  16. Melis E (1995) A model of analogy-driven proof-plan construction. In: Proceedings of the 14th international joint conference on artificial intelligence (IJCAI’95), Montréal, pp 182–189

  17. Melis E, Lieber J, Napoli A (1998) Reformulation in case-based reasoning. In: Smyth B, Cunningham P (eds) Fourth European workshop on case-based reasoning, EWCBR-98. Lecture notes in artificial intelligence, vol 1488. Springer, Berlin, pp 172–183

    Google Scholar 

  18. Lieber J (2002) 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-02), Lyon, France. IOS, Amsterdam, pp 81–85

    Google Scholar 

  19. Smyth B, Keane MT (1996) Using adaptation knowledge to retrieve and adapt design cases. Knowl-Based Syst 9(2):127–135

    Article  Google Scholar 

  20. Dubois D, Prade H, Sabbadin R (2001) Decision-theoretic foundations of qualitative possibility theory. Eur J Oper Res 128:459–478

    Article  MATH  MathSciNet  Google Scholar 

  21. Wald A (1950) Statistical decision functions. Wiley, New York

    MATH  Google Scholar 

  22. Hammond KJ (1990) Explaining and repairing plans that fail. AI Mag 45(1–2):173–228

    Google Scholar 

  23. d’Aquin M, Lieber J, Napoli A (2006) Adaptation knowledge acquisition: a case study for case-based decision support in oncology. In: Bichindaritz I, Marling C (eds) Special issue on CBR in the health sciences. Comput Intell 22(3–4):161–176

  24. Straccia U (2006) A fuzzy description logic for the semantic web. In: Sanchez E (ed) Fuzzy logic and the semantic web. Elsevier, Amsterdam, Chap 4, pp 73–90

    Google Scholar 

  25. d’Aquin M, Lieber J, Napoli A (2006) Towards a semantic portal for oncology using a description logic with fuzzy concrete domains. In: Sanchez E (ed) Fuzzy logic and the semantic web. Elsevier, Amsterdam, Chap 19, pp 379–393

    Google Scholar 

  26. Bouquet P, Giunchiglia F, van Harmelen F, Serafini L, Stuckenschmidt H (2004) Contextualizing ontologies. J Web Semant 1(4):1–19

    Google Scholar 

  27. Borgida A, Serafini L (2002) Distributed description logics: directed domain correspondences in federated information sources. In: Proceedings of the international conference on cooperative information systems

  28. d’Aquin M, Badra F, Lafrogne S, Lieber J, Napoli A, Szathmary L (2006) Adaptation knowledge discovery from a case base. In: Brewka G (ed) Proceedings of the 17th European conference on artificial intelligence (ECAI-06), Trento. IOS, Amsterdam, pp 795–796

    Google Scholar 

  29. Hanney K, Keane MT (1996) Learning adaptation rules from a case-base. In: Smith I, Faltings B (eds) Advances in case-based reasoning—third European workshop, EWCBR’96. Lecture notes in artificial intelligence, vol 1168. Springer, Berlin, pp 179–192

    Chapter  Google Scholar 

  30. Dunham MH (2003) Data mining—introductory and advanced topics. Prentice Hall, Upper Saddle River

    Google Scholar 

  31. Carbonell JG (1983) Learning by analogy: formulating and generalizing plans from past experience. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning, an artificial intelligence approach. Kaufmann, Los Altos, Chap 5, pp 137–161

    Google Scholar 

  32. Zaki MJ, Hsiao C-J (2002) CHARM: an efficient algorithm for closed itemset mining. In: SIAM international conference on data mining SDM’02, pp 33–43

  33. Szathmary L, Napoli A (2005) Coron: a framework for levelwise itemset mining algorithms. In: Ganter B, Godin R, Mephu Nguifo E (eds) Supplementary proceedings of the third international conference on formal concept analysis—ICFCA’05, Lens, France, pp 110–113

  34. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI (1996) Fast discovery of association rules. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. Menlo Park, CA. AAAI/MIT, Cambridge, pp 307–328

    Google Scholar 

  35. d’Aquin M, Lieber J, Napoli A (2006) Case-based reasoning within semantic web technologies. In: Twelfth international conference on artificial intelligence: methodology, systems, applications (AIMSA-06), pp 190–200

  36. Straccia U (2006) http://gaia.isti.cnr.it/~straccia. Last consultation: October 2006

  37. Dubois D, Mengin J, Prade H (2006) Possibilistic uncertainty and fuzzy features in description logic. A preliminary discussion. In: Sanchez E (ed) Fuzzy logic and the semantic web. Elsevier, Amsterdam, Chap 6, pp 101–113

    Google Scholar 

  38. Serafini L, Tamilin A (2005) DRAGO: Distributed reasoning architecture for the semantic web. In: Gomez-Perez A, Euzenat J (eds) Proceedings of the second European semantic web conference (ESWC’05). Lecture notes in computer science, vol 3532. Springer, Berlin, pp 361–376

    Google Scholar 

  39. Schmidt R, Vorobieva O (2005) Adaptation and medical case-based reasoning, focusing on endocrine therapy support. In: Artificial intelligence in medicine (AIME’05). Springer, Berlin

    Google Scholar 

  40. Doyle D, Cunningham P, Walsh P (2005) An evaluation of the usefulness of explanation in a CBR system for decision support in bronchiolitis treatment. In: Bichindaritz I, Marling C (eds) Proceedings of the workshop on CBR in the health sciences of the 6th international conference on case-based reasoning (ICCBR-05)

  41. Bichindaritz I, Marling C (2006) Case-based reasoning in the health sciences: what’s next? Artif Intell Med 36(2):127–135

    Article  Google Scholar 

  42. Montani S (2006) On the possible roles of case-based reasoning in medical decision support. In: Proceedings of the workshop on CBR in the health sciences of the 8th European conference on case-based reasoning (ECCBR 06). Springer, Berlin, pp 138–150

    Google Scholar 

  43. Bichindaritz I (2006) Mémoire: a framework for semantic interoperability of case-based reasoning systems in biology and medicine. Artif Intell Med 36(2):177–192

    Article  Google Scholar 

  44. d’Aquin M (2005) Un portail sémantique pour la gestion des connaissances en cancérologie. Thèse d’université, Université Henri Poincaré Nancy 1, soutenue le 15 décembre 2005

  45. Bichindaritz I, Kansu E, Sullivan KM (1998) Case-based reasoning in CARE-PARTNER: gathering experience for evidence-based medical practice. In: Smyth B, Cunningham P (eds) Fourth European workshop on case-based reasoning, EWCBR-98. Lecture notes in artificial intelligence, vol 1488. Springer, Berlin, pp 334–345

    Google Scholar 

  46. Bichindaritz I (2006) Prototypical case mining from medical literature. In: Proceedings of the workshop on CBR in the health sciences of the 8th European conference on case-based reasoning (ECCBR 06). Springer, Berlin, pp 123–137

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Orpailleur team, LORIA (UMR 7503 CNRS-INPL-INRIA-Nancy 2-UHP), BP 239, 54 506, Vandoeuvre-les-Nancy, France

    Jean Lieber, Mathieu d’Aquin, Fadi Badra & Amedeo Napoli

  2. Knowledge Media Institute (KMi), The Open University, Milton Keynes, UK

    Mathieu d’Aquin

Authors
  1. Jean Lieber
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mathieu d’Aquin
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fadi Badra
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Amedeo Napoli
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jean Lieber.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lieber, J., d’Aquin, M., Badra, F. et al. Modeling adaptation of breast cancer treatment decision protocols in the Kasimir project. Appl Intell 28, 261–274 (2008). https://doi.org/10.1007/s10489-007-0070-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-007-0070-2

Keywords