Rule Discovery in Large Time-Series Medical Databases

Abstract

AbstractSince 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 combination 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.KeywordsAverage MethodContinuous AttributeMotor Neuron DiseaseHospital Information SystemCategorical AttributeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.