Since hospital information systems have been introduced in large hospitals,a large amount of data,including laboratory examinations,have been stored as temporal databases.The characteristics of these temporal databases are:(1) Each record are inhomogeneous with respect to time-series,including short-term effects and long-term effects.(2)Each record has more than 1000 attributes when a patient is followed for more than one year.(3) When a patient is admitted for a long time,a large amount of data is stored in a very short term.Even medical experts cannot deal with these large databases,the interest in mining some useful information from the data are growing.In this paper,we introduce a combinatin of extended moving average method and rule induction method,called CEARI to discover new knowledge in temporal databases.this CEARI was applied to a medical dataset on Motor Neuron Diseases,the results of which show that interesting knowledge is discovered from each database.
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