27 March 2001 Medical knowledge discovery in hospital information systems
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Abstract
Since early 1980's, the rapid growth of hospital information systems stores the large amount of laboratory examinations as databases. Thus, it is highly expected that knowledge discovery and data mining (KDD) methods will find interesting patterns from databases as reuse of stored data and be important for medical research and practice because human beings cannot deal with such a huge amount of data. However, there are still few empirical approaches which discuss the whole data mining process from the viewpoint of medical data. In this paper, KDD process from a hospital information system is presented by using two medical datasets. This empirical study shows that preprocessing and data projection are the most time-consuming processes, in which very few data mining researches have not discussed yet and that application of rule induction methods is much easier than preprocessing.
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Shusaku Tsumoto, "Medical knowledge discovery in hospital information systems", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421077; https://doi.org/10.1117/12.421077
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