Mining Unexpected Associations for Signalling Potential Adverse Drug Reactions from Administrative Health Databases
Adverse reactions to drugs are a leading cause of hospitalisation and death worldwide. Most post-marketing Adverse Drug Reaction (ADR) detection techniques analyse spontaneous ADR reports which underestimate ADRs significantly. This paper aims to signal ADRs from administrative health databases in which data are collected routinely and are readily available. We introduce a new knowledge representation, Unexpected Temporal Association Rules (UTARs), to describe patterns characteristic of ADRs....[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006)|
|01_Jin_Mining_Unexpected_Associations_2006.pdf||482.6 kB||Adobe PDF||Request a copy|
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