Advertisement

Optimal Decision Explanation by Extracting Regularity Patterns

  • Concha Bielza
  • Juan A. Fernández del Pozo
  • Peter Lucas
Conference paper

Abstract

When solving decision-making problems with modern graphical models like influence diagrams, we obtain the decision tables with optimal decision alternatives. For real-life clinical problems, these tables are often extremely large, hindering the understanding of the reasons behind their content. KBM2L lists are new structures that simultaneously minimise memory storage space of these tables, and search for a better knowledge organisation. In this paper, we study the application of KBM2L lists in finding and thoroughly studying the optimal treatments for gastric nonHodgkin lymphoma. This is a difficult clinical problem, mainly because of the uncertainties involved. The resultant lists provide high-level explanations of optimal treatments for the disease, and are also able to find relationships between groups of variables and treatments.

Keywords

Fixed Part Decision Table Decision Node Optimal Alternative Neonatal Jaundice 
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]
    Shachter, R.D.: Evaluating Influence Diagrams. Operations Research 346 (1986) 871–882MathSciNetCrossRefGoogle Scholar
  2. [2]
    Fernandez del Pozo, J.A., Bielza, C., Gomez, M.: A List-Based Compact Representation for Large Decision Tables Management. European Journal of Operational Research (2003) to appearGoogle Scholar
  3. [3]
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. 2nd edition. Wiley, New York (2001)MATHGoogle Scholar
  4. [4]
    Kohavi, R.: Bottom-Up Induction of Oblivious Read-Once Decision Graphs. In: Bergadano, F., De Raedt, L. (eds.): Machine Learning: ECML-94. Lecture Notes in Computer Science, Vol. 784. Springer-Verlag, Berlin (1994) 154–169Google Scholar
  5. [5]
    Pawlak, Z.: Rough Set Approach to Knowledge-Based Decision Support. European Journal of Operational Research 99(1997) 48–57MATHCrossRefGoogle Scholar
  6. [6]
    Lauritzen, S., Nilsson, D.: Representing and Solving Decision Problems with Limited Information. Management Science 479 (2001) 1235–1251MATHCrossRefGoogle Scholar
  7. [7]
    Vomlelova, M., Jensen, F.V.: An Extension of Lazy Evaluation for Influence Diagrams Avoiding Redundant Variables in the Potentials. In: Gamez, J.A., Salmeron, A. (eds.): Proc. of the 1st European Workshop on Probabilistic Graphical Models, University of Castilla-LaMancha, Spain (2002) 186–193Google Scholar
  8. [8]
    Lucas, P., Boot, H., Taal, B.: Computer-Based Decision-Support in the Management of Primary Gastric non-Hodgkin Lymphoma. Methods of Information in Medicine 37 (1998) 206–219Google Scholar
  9. [9]
    Bielza, C., Fernandez del Pozo, J.A., Lucas, P.: Finding and Explaining Optimal Treatments. In: Dojat, M., Keravnou, E., Barahona, P. (eds.): Artificial Intelligence in Medicine, Proc. 9th Conference on Artificial Intelligence in Medicine in Europe, AIME 2003. Lecture Notes in Computer Science, Springer to appearGoogle Scholar
  10. [10]
    Knuth, D.E.: The Art of Computer Programming, Vol. 1: Fundamental Algorithms. Addison-Wesley, Reading (1968)Google Scholar
  11. [11]
    Eidt, S., Stolte, M., Fishcer, R.: Helicobacter Pylori Gastritis and Primary Gastric non-Hodgkin’s Lymphoma. Journal of Clinical Pathology 41 (1994) 436–439CrossRefGoogle Scholar
  12. [12]
    Cooper G.F.: A Method for Using Belief Networks as Influence Diagrams. In: Proceedings of the Workshop on Uncertainty in Artificial Intelligence, Minneapolis, Minnesota, (1988) 55–63Google Scholar
  13. [13]
    Farquhar, P.H.: Utility Assessment Methods. Management Science 30 (1984) 1283–1300MathSciNetMATHCrossRefGoogle Scholar
  14. [14]
    Bielza, C., Gomez, M., Rios-Insua, S., Fernandez del Pozo, J.A.: Structural, Elicitation and Computational Issues Faced when Solving Complex Decision Making Problems with Influence Diagrams. Computers & Operations Research 277-8 (2000) 725–740MATHCrossRefGoogle Scholar
  15. [15]
    Fernandez del Pozo, J.A., Bielza, C., Gomez, M.: Knowledge Organisation in a Neonatal Jaundice Decision Support System. In: Crespo, J., Maojo, V., Martin, F. (eds.): Medical Data Analysis. Lecture Notes in Computer Science, Vol. 2199. Springer-Verlag, Berlin (2001) 88–94Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Concha Bielza
    • 1
  • Juan A. Fernández del Pozo
    • 1
  • Peter Lucas
    • 2
  1. 1.Decision Analysis GroupTechnical University of Madrid, Campus de MontegancedoMadridSpain
  2. 2.Institute for Computing and Information SciencesUniversity of NijmegenNijmegenThe Netherlands

Personalised recommendations