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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Allen, J.: Maintaining Knowledge About Temporal Intervals. Communications of the ACM 26(11), 832–843 (1983)
Balaban, M., Boaz, D., Shahar, Y.: Applying temporal abstraction in medical information systems. Annals of mathematics, computing and teleinformatics 1(1), 56–64 (2003)
Bellazzi, R., et al.: Intelligent Analysis of Clinic Time Series: An Application in the Diabetes Mellitus Domain. Intelligence in Medicine 20, 37–57 (2000)
Ho, T.B., et al.: Mining Hepatitis Data with Temporal Abstraction. In: ACM International Conference on Knowledge Discovery and Data Mining KDD’03, pp. 369–377. ACM Press, New York (2003)
Hoppner, F.: Learning dependencies in multivariate times series. In: ECAI’02 Workshop on Knowledge Discovery in (Spatio)-Temporal Data, pp. 25–31 (2002)
Murawaki, Y., et al.: Comparison of clinical laboratory liver tests between asymptomatic HBV and HCV carriers with persistently normal amino-transferase serum levels. Hepatol Research 21(1), 67–75 (2001)
Sakai, H., et al.: Metabolism of hemoglobin-vesicles (artificial oxygen carriers) and their influence on organ functions in a rat model. Biomaterials 25(18), 4317–4325 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)