Granular Rules for Medical Diagnosis
This paper discusses granular models of medical diagnostic rules which is an extension of rough set rule model. Medical diagnostic reasoning is characterized by three processes: focusing mechanism, differential diagnosis and detection of complications. First, focusing mechanism uses a set of symptoms which are always observed by almost all the cases of a candidate and if a case does not include any one of them, the candidate will be rejected. Second, from selected candidates, a set of symptoms which are highly observed in the cases are used for confirming the differential diagnosis. Finally, detection of complications is a set of symptoms whose occurrence of a candidate is very low but are very important for diagnosis of other diseases. These rule models can be easily described by an extension of rough set model: supporting sets of the first two sets of symptoms correspond to upper and lower approximations of a target concept. The final one is described by interrelations between a target concept and other concepts, which will be a new type of information granules.
The author would like to thank past Professor Pawlak for all the comments on my research and his encouragement. Without his influence, one of the authors would neither have received Ph.D on computer science, nor become a professor of medical informatics. The author also would like to thank Professor Jerzy Grzymala-Busse, Andrezj Skowron, Roman Slowinski, Yiyu Yao, Guoyin Wang, Wojciech Ziarko for their insightful comments.
- 1.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.) LP. Lecture Notes in Computer Science, vol. 221, pp. 287–298. Springer (1985)Google Scholar
- 3.Pawlak, Z.: Rough modus ponens. In: Proceedings of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems 98. Paris (1998)Google Scholar
- 4.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. Wiley, New York (1994)Google Scholar
- 8.Tsumoto, S.: Automated discovery of positive and negative knowledge in clinical databases based on rough set model (2000)Google Scholar
- 9.Tsumoto, S.: Extraction of hierarchical decision rules from clinical databases using rough sets. Inf. Sci. (2003)Google Scholar
- 11.Tsumoto, S.: Rough sets and medical differential diagnosis. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems. Intelligent Systems Reference Library, vol. 42, pp. 605–621. Springer (2013)Google Scholar
- 12.Tsumoto, S., Tanaka, H.: Induction of probabilistic rules based on rough set theory. In: Jantke, K.P., Kobayashi, S., Tomita, E., Yokomori, T. (eds.) Algorithmic Learning Theory, 4th International Workshop, ALT 1993, Tokyo, Japan, 8-10 November 1993, Proceedings. Lecture Notes in Computer Science, vol. 744, pp. 410–423. Springer (1993). https://doi.org/10.1007/3-540-57370-4Google Scholar
- 13.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 96, pp. 63–69. AAAI Press, Palo Alto (1996)Google Scholar