Abstract
The integration of rule-based and case-based reasoning is particularly useful in medical applications, where both general rules and specific patient cases are usually available. In the present paper we aim at presenting a decision support tool for Insulin Dependent Diabetes Mellitus management relying on such a kind of integration. This multi-modal reasoning system aims at providing physicians with a suitable solution to the problem of therapy planning by exploiting, in the most flexible way, the strengths of the two selected methods. In particular, the integration is pursued without considering one of the modality as the most prominent reasoning method, but exploiting complementarity in all possible ways. In fact, while rules provide suggestions on the basis of a situation detection mechanism that relies on structured prior knowledge, CBR may be used to specialize and dynamically adapt the rules on the basis of the patient’s characteristics and of the accumulated experience. On the other hand, if a particular patient class is not sufficiently covered by cases, the use of rules may be exploited to try to learn suitable situations, in order to improve the competence of the case-based component. Such a work will be integrated in the EU funded project T-IDDM architecture, and has been preliminary tested on a set of cases generated by a diabetic patient simulator.
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References
D. Aha and J. Daniels (eds.). Proc. AAAI Workshop on CBR Integrations. AAAI Press, 1998.
R. Bellazzi, S. Montani, and L. Portinale. Retrieval in a prototype-based case library: a case study in diabetes therapy revision. In Proc. 4th EWCBR, LNAI 1488, pages 64–75. Springer Verlag, 1998.
I. Bichindaritz, E. Kansu, and K.M. Sullivan. Case-based reasoning in CARE-PARTNER: gathering evidence for evidence-based medical practice. In Springer Varlag, editor, Proc. 4th EWCBR, LNAI 1488, pages 334–345, 1998.
P.P. Bonissone and S. Dutta. Integrating case-based and rule-based reasoning: the possibilistic connection. In Proc. 6th Conf. on Uncertainty in Artificial Intelligence, Cambridge, MA, 1990.
L.K. Branting and B.W. Porter. Rules and precedents as complementary warrants. In Proc. 9th National Conference on Artificial Intelligence (AAAI 91), Anaheim, 1991.
C. Cobelli, G. Nucci, and S. Del Prato. A physiological simulation model of the glucose-insulin system in type 1 diabetes. Diabetes Nutrition and Metabolism, 11, 1998.
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, 329:977–986, 1993.
P. Domingos and M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103–130, 1997.
E. Freuder (ed.). AAAI Spring Symposium on Multi-modal Reasoning. AAAI Press, 1998.
J.L. Kolodner. Case-Based Reasoning. Morgan Kaufmann, 1993.
P. Koton. Integrating causal and case-based reasoning for clinical problem solving. In Proc of the AAAI Symposium on Artificial Intelligence inMedicine, pages 53–54, Stanford, 1988.
C. Larizza, R. Bellazzi, and A. Riva. Temporal abstractions for diabetic patients management. In LNAI 1211, pages 319–330. Springer Verlag, 1997.
D. Macchion and D.P. Vo. A hybrid KBS for technical diagnosis learning and assistance. In Lecture Notes in Artificial Intelligence 837, pages 301–312. Springer Verlag, 1993.
S. Montani, R. Bellazzi, C Larizza, A. Riva, G. d’Annunzio, S. Fiocchi, R. Lorini, and M. Stefanelli. Protocol-based reasoning in diabetic patient management. International Journal of Medical Informatics, 53:61–77, 1999.
S. Montani, R. Bellazzi, L. Portinale, S. Fiocchi, and M. Stefanelli. A case-based retrieval system for diabetic patients therapy. In Proceedings of IDAMAP 98 work-shop, ECAI 98, pages 64–70, Brighton, 1998.
L. Portinale, P. Torasso, and D. Magro. Selecting most adaptable diagnostic solutions through Pivoting-Based Retrieval. In Proc. 2nd ICCBR, LNAI 1266, pages 393–402.Springer Verlag, 1997.
M. Ramoni and P. Sebastiani. The use of exogenous knowledge to learn bayesian networks for incomplete databases. In Advances in data Analysis, LNCS, pages 537–548. Springer Verlag, 1997.
E.L. Rissland and D.B. Skalak. Combining case-based and rule-based reasoning: a heuristic approach. In Proc. 11th IJCAI, pages 524–530, Detroit, 1989.
A. Riva, R. Bellazzi, and M. Stefanelli. web-based system for the intelligent management of diabetic patients. MD Computing, 14:360–364, 1997.
R. Schmidt and L. Gierl. Experiences with prototype designs and retrieval methods in medical Case-Based Reasoning systems. In Proc. 4th EWCBR, LNAI 1488, pages 370–381. Springer Verlag, 1998.
J. Surma and K. Vanhoff. Integrating rules and cases for the classification task. In Proc. 1st ICCBR, LNAI 1010, pages 325–334. Springer Verlag, 1995.
D.R. Wilson and T.R. Martinez. Improved heterogeneous distance functions. Journal of Artificial Intelligence Research, 6:1–34, 1997.
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Bellazzi, R., Montani, S., Portinale, L., Riva1, A. (1999). Integrating Rule-Based and Case-Based Decision Making in Diabetic Patient Management⋆. In: Althoff, KD., Bergmann, R., Branting, L. (eds) Case-Based Reasoning Research and Development. ICCBR 1999. Lecture Notes in Computer Science, vol 1650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48508-2_28
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DOI: https://doi.org/10.1007/3-540-48508-2_28
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