Abstract
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 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.
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Keywords
- Average Method
- Continuous Attribute
- Motor Neuron Disease
- Hospital Information System
- Categorical Attribute
These 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.
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Tsumoto, S. (1999). Rule Discovery in Large Time-Series Medical Databases. In: Żytkow, J.M., Rauch, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1999. Lecture Notes in Computer Science(), vol 1704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48247-5_3
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DOI: https://doi.org/10.1007/978-3-540-48247-5_3
Publisher Name: Springer, Berlin, Heidelberg
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