Abstract
In medicine many exceptions occur. In medical practise and in knowledge-based systems too, it is necessary to consider them and to deal with them appropriately. In medical studies and in research exceptions shall be explained. We present a system that helps to explain cases that do not fit into a theoretical hypothesis. Our starting points are situations where neither a well-developed theory nor reliable knowledge nor a proper case base is available. So, instead of reliable theoretical knowledge and intelligent experience, we have just some theoretical hypothesis and a set of measurements.
In this paper, we propose to combine CBR with a statistical model. We use CBR to explain those cases that do not fit the model. The case base has to be set up incrementally, it contains the exceptional cases, and their explanations are the solutions, which can be used to help to explain further exceptional cases.
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References
Schmidt, R., Vorobieva, O.: ISOR: A Case-Based System for Investigations of Therapy Inefficacy. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4251, pp. 334–341. Springer, Heidelberg (2006)
Kendall, M.G., Stuart, A.: The advanced theory of statistics, 4th edn. Macmillan publishing, New York (1979)
Hai, G.A.: Logic of diagnostic and decision making in clinical medicine. Politheknica publishing, St. Petersburg (2002)
Bichindaritz, I., Kansu, E., Sullivan, K.M.: Case-based Reasoning in Care-Partner. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 334–345. Springer, Heidelberg (1998)
Prentzas, J., Hatzilgeroudis, I.: Integrating Hybrid Rule-Based with Case-Based Reasoning. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 336–349. Springer, Heidelberg (2002)
Shuguang, L., Qing, J., George, C.: Combining case-based and model-based reasoning: a formal specification. In: Proc APSEC 2000, p. 416 (2000)
Corchado, J.M., Corchado, E.S., Aiken, J., et al.: Maximum likelihood Hebbian learning based retrieval method for CBR systems. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 107–121. Springer, Heidelberg (2003)
Rezvani, S., Prasad, G.: A hybrid system with multivariate data validation and Case-based Reasoning for an efficient and realistic product formulation. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 465–478. Springer, Heidelberg (2003)
Arshadi, N., Jurisica, I.: Data Mining for Case-based Reasoning in high-dimensional biological domains. IEEE Transactions on Knowledge and Data Engineering 17(8), 1127–1137 (2005)
Davidson, A.M., Cameron, J.S., Grünfeld, J.-P., et al. (eds.): Oxford Textbook of Nephrology, vol. 3. Oxford University Press, Oxford (2005)
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Vorobieva, O., Schmidt, R. (2008). CBR to Explain Medical Model Exceptions. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85563-7_38
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DOI: https://doi.org/10.1007/978-3-540-85563-7_38
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