Abstract
This paper overviews the following two important issues on the correspondence between Pawlak’s rough set model and medical reasoning. The first main idea of rough sets is that a given concept can be approximated by partition-based knowledge as upper and lower approximation. Interestingly, thes approximations correspond to the focusing mechanism of differential medical diagnosis; upper approximation as selection of candidates and lower approximation as concluding a final diagnosis. The second idea of rough sets is that a concept, observations can be represented as partitions in a given data set, where rough sets provides a rule induction method from a given data. Thus, this model can be used to extract rule-based knowledge from medical databases. Especially, rule induction based on the focusing mechanism is obtained in a natural way.
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References
Pawlak, Z.: Rough Sets. Kluwer Academic Publishers, Dordrecht (1991)
Matsudo city hospital (in japanese), http://www.intership.ne.jp/~matuhosp/
Matsumura, Y., Matsunaga, T., Hata, Y., Kimura, M., Matsumura, H.: Consultation system for diagnoses of headache and facial pain: Rhinos. Medical Informatics 11, 147–157 (1988)
Icot archive, http://www.icot.or.jp/English-HomePage.html
Matsumura, Y., Matsunaga, T., Maeda, Y., Tsumoto, S., Matsumura, H., Kimura, M.: Consultation system for diagnosis of headache and facial pain: ”rhinos”. In: Wada, E. (ed.) Logic Programming 1985. LNCS, vol. 221, pp. 287–298. Springer, Heidelberg (1986)
Tsumoto, S., Tanaka, H.: Primerose: Probabilistic rule induction method based on rough set theory. In: Ziarko, W. (ed.) RSKD. Workshops in Computing, pp. 274–281. Springer, Heidelberg (1993)
Tsumoto, S., Tanaka, H.: Automated discovery of medical expert system rules from clinical databases based on rough sets. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining 1996, Palo Alto, pp. 63–69. AAAI Press, Menlo Park (1996)
Tsumoto, S.: Modelling medical diagnostic rules based on rough sets. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS, vol. 1424, pp. 475–482. Springer, Heidelberg (1998)
Tsumoto, S.: Automated extraction of medical expert system rules from clinical databases on rough set theory. Inf. Sci. 112(1-4), 67–84 (1998)
Skowron, A., Grzymala-Busse, J.: From rough set theory to evidence theory. In: Yager, R., Fedrizzi, M., Kacprzyk, J. (eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 193–236. John Wiley & Sons, New York (1994)
Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46(1), 39–59 (1993)
Pawlak, Z.: Rough modus ponens. In: Proceedings of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems 1998, Paris (1998)
Tsumoto, S.: Extraction of experts’ decision rules from clinical databases using rough set model. Intelligent Data Analysis 2(3) (1998)
Tsumoto, S.: Extraction of structure of medical diagnosis from clinical data. Fundam. Inform. 59(2-3), 271–285 (2004)
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Tsumoto, S. (2006). Pawlak Rough Set Model, Medical Reasoning and Rule Mining. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_7
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DOI: https://doi.org/10.1007/11908029_7
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