Maintaining knowledge about temporal intervals
Communications of the ACM
Mining hepatitis data with temporal abstraction
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Intelligent analysis of clinical time series: an application in the diabetes mellitus domain
Artificial Intelligence in Medicine
Integration of Learning Methods, Medical Literature and Expert Inspection in Medical Data Mining
IEICE - Transactions on Information and Systems
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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.