Abstract
One of the most important characteristics of chance discovery is that it focuses on the specific events or patterns in which the essential nature of an applied domain is implicitly included. The understanding and forecasting of such patterns and events will have a significant impact on decision making in the applied domain. This paper discusses the meaning of chance discovery from the viewpoint of medicine. Since chance discovery in medicine can be viewed as the way to find a suitable occasion for some critical actions or to check the dangerous possibilities, called rare risky events, detection and interpretation of rare but important events are ones of the components that supports chance discovery. According to this observation, several approaches for detecting rare events were introduced and evaluated by a small dataset on neurological diseases. Experimental results show that a set of events which include rare risky events can be detected by the introduced detection method, though interpretation by domain experts is required for selection of such events.
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Abiteboul, S., Hull, R. and Vianu, V.,Foundations of Databases, Addison-Wesley, 1995.
Adams, R. D. and Victor, M.,Principles of Neurology, 5th ed., McGraw-Hill, 1993.
Das, G., Lin, K. I., Mannila, H., Renganathan, G., and Smyth, P., “Rule Discovery from Time Series,” inProc. of Fourth International Conference on Knowledge Discovery and Data Mining, pp. 16–22, 1998.
Advances in Knowledge Discovery and Data Mining (Fayyad, U. M., et al. eds.), AAAI Press, 1996.
Hamilton, J. D.,Time Series Analysis, Princeton University Press, 1994.
Miksch, S., Horn, W., Popow, C. and Paky, F., “Utilizing Temporal Data Abstraction for Data Validation and Therapy Planning for Artificially Ventilated Newborn Infants,”Artificial Intelligentce in Medicine, 8, 543–576, 1996.
Osawa, Y., “Chance Discoveries for Making Decisions in Complex Real World,”New Generation Computing, 20, 2, Springer-Verlag, pp. 143–164, 2002.
Pawlak, Z.,Rough Sets. Kluwer Academic Publishers, 1991.
Skowron, A. and Grzymala-Busse, J., “From Rough Set Theory to Evidence Theory,” (Yager, R., Fedrizzi, M. and Kacprzyk, J., eds.)Advances in the Dempster-Shafer Theory of Evidence, pp. 193–236, John Wiley & Sons, New York, 1994.
Tsumoto, S. and Tanaka, H., “PRIMEROSE: Probabilistic Rule Induction Method based on Rough Sets and Resampling Methods,”Computational Intelligence, 11, pp. 389–405, 1995.
Tsumoto, S., “Automated Induction of Medical Expert System Rules from Clinical Databases based on Rough Set Theory,”Information Sciences, 112, pp. 67–84, 1998.
Tsumoto, S. and Takabayashi, K., “Comparison and Evaluation of Knowledge Acquired by KDD Methods,”Japanese Society of AI, 15, pp. 790–797, 2000. (in Japanese).
Tsumoto, S., “Automated Discovery of Positive and Negative Knowledge in Clinical Databases based on Rough Set Model,”IEEE Engineering in Medicine and Biology Magazine, 19, pp. 56–62, 2000.
Van Bemmel, J. and Musen, M. A.,Handbook of Medical Informatics, Springer-Verlag, 1997.
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Shusaku Tsumoto, Ph.D.: He graduated from Osaka University, School of Medicine in 1989. After residents of neurology in Chiba University Hospital, he was involved in developing hospital information system in Chiba University Hospital. He moved to Tokyo Medical University in 1993 and started his research on rough sets and data mining in medicine. He received his Ph.D (Computer Science) from Tokyo Institute of Technology in 1997, and is now a Professor at Department of Medical Informatics, Shimane Medical University. His interests include approximate reasoning, data mining, fuzzy sets, knowledge acquisition, mathematical theory of data mining, and rough sets (alphabetical order).
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Tsumoto, S. Chance discovery in medicine — Detection of rare risky events in chronic diseases —. New Gener Comput 21, 135–147 (2003). https://doi.org/10.1007/BF03037631
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DOI: https://doi.org/10.1007/BF03037631