Mining patterns of dyspepsia symptoms across time points using constraint association rules

Authors:
Annie Lau;Siew Siew Ong;Ashesh Mahidadia;Achim Hoffmann;Johanna Westbrook;Tatjana Zrimec
Affiliations:
Centre for Health Informatics, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;Centre for Health Informatics, The University of New South Wales, Sydney, NSW, Australia;Centre for Health Informatics, The University of New South Wales, Sydney, NSW, Australia
Venue:
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2003

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Abstract

In this paper, we develop and implement a framework for constraint-based association rule mining across subgroups in order to help a domain expert find useful patterns in a medical data set that includes temporal data. This work is motivated by the difficulties experienced in the medical domain to identify and track dyspepsia symptom clusters within and across time. Our framework, Apriori with Subgroup and Constraint (ASC), is built on top of the existing Apriori framework. We have identified four different types of phase-wise constraints for subgroups: constraint across subgroups, constraint on subgroup, constraint on pattern content and constraint on rule. ASC has been evaluated in a real-world medical scenario; analysis was conducted with the interaction of a domain expert. Although the framework is evaluated using a data set from the medical domain, it should be general enough to be applicable in other domains.