Integrating Different Methodologies for Insulin Therapy Support in Type 1 Diabetic Patients

  • Stefania Montani
  • Paolo Magni
  • Abdul V. Roudsari
  • Ewart R. Carson
  • Riccardo Bellazzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)


We propose a Multi Modal Reasoning (MMR) methodology designed to provide physicians with knowledge management and decision support functionality in the context of type 1 diabetes melli tus care. The MMR system performs a tight integration of Case Based Reasoning (CBR), Rule Based Reasoning (RBR) and Model Based Reasoning (MBR), with the aim of suggesting a therapy properly tailored to the patient’s needs, overcoming the single approaches’ limitations. This methodology allows the exploitation of the implicit knowledge embedded in patients’ visits (past cases) and in monitoring data through Case Based retrieval. Moreover the explicit domain knowledge is formalized in a set of production rules and in a mathematical model. The system has been preliminary tested both on simulated and on real patients’ data.


Blood Glucose Level Case Base Reasoning Intensive Insulin Therapy Case Library Past Case 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aha, D., and Daniels, J. (eds.): Proc. AAAIWorkshop on CBR Integrations, AAAI Press (1998)Google Scholar
  2. 2.
    Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann (1993)Google Scholar
  3. 3.
    Steels, L.: Corporate knowledge management. Proc. of ISMICK’ 93, Compiegne (1993) 9–30Google Scholar
  4. 4.
    Surma, J., Vanhoff, K.: Integrating rules and cases for the classification task. In: LNAI 1010, Proc. ICCBR, Springer Verlag (1995) 325–334Google Scholar
  5. 5.
    Xu, L.D.: An integrated rule-and case-based approach to AIDS initial assessment. International Journal of Biomedical Computing 40 (1996) 197–207CrossRefGoogle Scholar
  6. 6.
    Branting, L.K., Porter, B.W.: Rules and precedents as complementary warrants. Proc. AAAI 91, Anaheim (1991)Google Scholar
  7. 7.
    Bichindaritz, I., Kansu, E., Sullivan, K.M.: Case-based reasoning in CARE-PARTNER: gathering evidence for evidence-based medical practice. In: LNAI 1488, Proc. 4th EWCBR, Springer Verlag (1998) 334–345Google Scholar
  8. 8.
    Leake, D.B.: Combining rules and cases to learn case adaptation. Proc. 17th International Conference of Cognitive Science Society, Pittsburgh (1995)Google Scholar
  9. 9.
    Sary, C., et al.: Trend analysis for spacecraft systems using multimodal reasoning. Proc. AAAI Spring Symp. on Multi-modal Reasoning, AAAI Press (1998) 157–162Google Scholar
  10. 10.
    Russel, S., Norvig, P.: Artificial Intelligence, a modern approach. Prentice Hall (1995)Google Scholar
  11. 11.
    Montani, S., Bellazzi, R., Portinale, L., Stefanelli, M.: A Multi-Modal Reasoning Methodology for Managing IDDM Patients. International Journal of Medical Informatics 58-59 (2000) 243–256CrossRefGoogle Scholar
  12. 12.
    The Diabetes Control and Complication Trial Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The New England Journal of Medicine 329 (1993) 977–986CrossRefGoogle Scholar
  13. 13.
    Deutsch, T., Roudsari, A.V., Leicester, H.J., Theodorou, T., Carson, E.R., and Sonksen, P.H.: UTOPIA: a consultation system for visit by visit diabetes management. Medical Informatics 21 (1996) 345–358CrossRefGoogle Scholar
  14. 14.
    Andreassen, S., Benn, J., Hovorka, R., Olesen, K.G., Carson, E.R.: A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. Computer Methods and Programs in Biomedicine 41 (1994) 153–165CrossRefGoogle Scholar
  15. 15.
    Ramoni, M., Sebastiani, P.: The use of exogenous knowledge to learn Bayesian Networks for incomplete databases. In: Liu, X., Cohen, P., and Berthold, M. (eds.): Advances In Intelligent Data Analysis, Springer Verlag, Berlin (1997) 537–548CrossRefGoogle Scholar
  16. 16.
    Hovorka, R., Svacina, S., Carson, E.R., Williams, C.D., Sönksen, P.H.: A consultation system for insulin therapy. Computer Methods and Programs in Biomedicine 32 (1996) 303–310CrossRefGoogle Scholar
  17. 17.
    Nucci, G., et al.: Verification phase final report, T-IDDM deliverable 5.2.
  18. 18.
    Cobelli, C., Nucci, G., Del Prato, S.: A physiological simulation model in type I Diabetes. Diabetes nutrition and Metabolism 11 (1998) 78Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Stefania Montani
    • 1
  • Paolo Magni
    • 1
  • Abdul V. Roudsari
    • 2
  • Ewart R. Carson
    • 2
  • Riccardo Bellazzi
    • 1
  1. 1.Dipartimento di Informatica e Sistemistica Università di PaviaPaviaItaly
  2. 2.MIM CenterCity UniversityUK

Personalised recommendations