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Case-Based Reasoning Prognosis for Temporal Courses

  • R. Schmidt
  • L. Gierl
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 96)

Abstract

Since clinical management of patients and clinical research are essentially time-oriented endeavors, reasoning about time has recently become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with Case-Based Reasoning. The method was originally generated for multiparametric time course prognosis of the kidney function. Recently, we have started to apply the same ideas for the prognosis of the temporal spread of diseases. In this chapter, we mainly describe both applications and subsequently present a generalization of our method.

Keywords

Kidney Function Kidney Failure Retrieval Algorithm Weekly Data Obligatory Condition 
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.

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References

  1. [1]
    Robeson, S.M. and Steyn, D.G. (1990), “Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations,” Atmospheric Environment, vol. 24 B, no. 2, pp. 303–312.Google Scholar
  2. [2]
    Shahar,Y. (1999), “Timing is everything: temporal reasoning and temporal data maintenance in medicine,” Proceedings of AIMDM’99 Aalborg, Springer-Verlag, Berlin, pp. 30–46.Google Scholar
  3. [3]
    Haimowitz, I.J. and Kohane, I.S. (1993), “Automated trend detection with alternate temporal hypotheses,” Proceedings of the 13th International Joint Conference on Artificial Intelligence, Morgan Kaufmann Publishers, San Mateo, pp. 146–151.Google Scholar
  4. [4]
    Miksch, S., Horn, W., Popow, C., and Paky, F. (1995), “Therapy planning using qualitative trend descriptions,” Proceedings of the Fifth AIME Pavia, Springer Verlag, Berlin, pp. 197–208.Google Scholar
  5. [5]
    Schmidt, R., Boscher, L., Heindl, B., et al. (1995), “Adaptation and abstraction in a case-based antibiotics therapy adviser,” Proceedings of the Fifth AIME Pavia, Springer-Verlag, Berlin, pp. 209–217.Google Scholar
  6. [6]
    Swoboda, W., Zwiebel, F.M., Spitz, R., and Giert, L. (1994), “A case-based consultation system for postoperative management of liver-transplanted patients,” Proceedings of the 12th MIE Lisbon, IOS Press, Amsterdam, pp. 191–195.Google Scholar
  7. [7]
    Gierl, L. and Stengel-Rutkowski, S. (1994), “Integrating consultation and semi-automatic knowledge acquisition in a prototype-based architecture: experiences with dysmorphic syndromes,” Artificial Intelligence in Medicine, vol. 6, pp. 29–49.PubMedCrossRefGoogle Scholar
  8. [8]
    Lenz, M., Auriol, E., and Manago, M. (1998), “Diagnosis and decision support,” in: Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (Eds.), Case-Based Reasoning Technology, from Foundations to Applications, Springer-Verlag, Berlin, pp. 51–90.CrossRefGoogle Scholar
  9. [9]
    Smyth, B. and Keane, M.T. (1998), “Adaptation-guided retrieval: questioning the similarity assumption in reasoning,” Artificial Intelligence, vol. 102, pp. 249–293.CrossRefGoogle Scholar
  10. [10]
    Schmidt, R., Pollwein, B., and Gierl, L. (1999), “Medical multi-parametric time course prognoses applied to kidney function assessments,” International Journal in Medical Informatics, vol. 53, no. 2–3, pp. 253–264.CrossRefGoogle Scholar
  11. [11]
    Schmidt, R. and Gierl, L. (2001), “Temporal abstractions and Case-Based Reasoning for medical course data: two prognostic applications,” Proceedings of Second MLDM Leipzig, Springer-Verlag Berlin. ( To appear. )Google Scholar
  12. [12]
    Aamodt, A. and Plaza, E. (1994), “Case-Based Reasoning: foundation issues. Methodological variation-and system approaches,” AI Communications, vol. 7, no. 1, pp. 39–59.Google Scholar
  13. [13]
    Kolodner, J. (1993), Case-Based Reasoning, Morgan Kaufmann Publishers, San Mateo.Google Scholar
  14. [14]
    Macura, R. and Macura, K. (1995), “MacRad: radiology image resources with a case-based retrieval system,” Proceedings of the First ICCBR Sesimbra, Springer-Verlag, Berlin, pp. 43–54.Google Scholar
  15. [15]
    Quinlan, J.R. (1993), C4.5, Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo.Google Scholar
  16. [16]
    Broder, A.J. (1990), “Strategies for efficient incremental nearest neighbor search,” Pattern Recognition, vol. 23, pp. 171–178.CrossRefGoogle Scholar
  17. [17]
    Stottler, R.H., Henke, A.L., and King, J.A. (1989), “Rapid retrieval algorithms for Case-Based Reasoning,” Proceedings of the 11th IJCAI Detroit, Morgan Kaufmann Publishers, San Mateo, pp. 233–237.Google Scholar
  18. [18]
    Anderson, J.R. (1989), “A theory of the origins of human knowledge,” Artificial Intelligence, vol. 40, Special Volume on Machine Learning, pp. 313–351.CrossRefGoogle Scholar
  19. [19]
    Jones, E.K. and Roydhouse, A. (1994), “Iterative design for case retrieval systems,” Proceedings of AAAI Workshop on Case-Based Reasoning, Seattle, AAAI Press.Google Scholar
  20. [20]
    Lekkas, G.P., Arouris, N.M., and Viras, L.L. (1994), “Case-Based Reasoning in environmental monitoring applications,” Applied Artificial Intelligence, vol. 8, pp. 349–376.CrossRefGoogle Scholar
  21. [21]
    Lees, B. and Corchado, J. (1997), “Case based reasoning in a hybrid agent-oriented system,” Proceedings of the 5th GWCBR Kaiserslautern, University Press Kaiserslautern, pp. 139–144.Google Scholar
  22. [22]
    Wenkebach, U., Pollwein, B., and Finsterer, U. (1992), “Visualization of large datasets in intensive care,” Proc Annu Symp Comput Appl Med Care, pp. 18–22.Google Scholar
  23. [23]
    Tversky, A. (1977), “Features of similarity,” Psychological Review, vol. 84, pp. 327–352.CrossRefGoogle Scholar
  24. [24]
    DeSarbo, W.S. et al. (1992), “TSCALE: a new multidimensional scaling procedure based on Tversky’s contrast model,” Psychometrika, vol. 57, pp. 43–69.CrossRefGoogle Scholar
  25. [25]
    Farrington, C.P. and Beale, A.D. (1977), “The detection of outbreaks of infectious diseases,” in: Gierl, L. et al. (Eds.), International Workshop on Geomedical Systems Rostock, TeubnerVerlag, Stuttgart Leipzig, pp. 97–117.Google Scholar
  26. [26]
    Wilke, W., Smyth, B., and Cunningham, P. (1998), “Using configuration techniques for adaptation,” in: Lenz, M., BartschSpörl, B., Burkhard, H.-D., Wess, S. (Eds.), Case-Based Reasoning Technology, from Foundations to Applications, Springer-Verlag, Berlin, pp. 139–168.CrossRefGoogle Scholar
  27. [27]
    Schmidt, R. and Gierl, L. (2000), “Case-Based Reasoning for medical knowledge-based systems,” Proceedings of MIE and GMDS Hannover, IOS Press, Amsterdam, pp. 720–725.Google Scholar
  28. [28]
    Schmidt, R., Pollwein, B., and Gierl, L. (1999), “Experiences with Case-Based Reasoning methods and prototypes for medical knowledge-based systems,” Proceedings of AIMDM’99 Aalborg, Springer-Verlag, Berlin, pp. 124–132.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • R. Schmidt
  • L. Gierl

There are no affiliations available

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