Abstract
Various data mining methods have been developed last few years for hepatitis study using a large temporal and relational database given to the research community. In this work we introduce a novel temporal abstraction method to this study by detecting and exploiting temporal patterns and relations between events in viral hepatitis such as “event A slightly happened before event B and B simultaneously ended with event C”. We developed algorithms to first detect significant temporal patterns in temporal sequences and then to identify temporal relations between these temporal patterns. Many findings by data mining methods show to be significant by physician evaluation and match with reported results in Medline.
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© 2007 Springer Berlin Heidelberg
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Ho, T.B., Nguyen, C.H., Kawasaki, S., Takabayashi, K. (2007). Temporal Relations Extraction in Mining Hepatitis Data. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_54
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DOI: https://doi.org/10.1007/978-3-540-71701-0_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
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