Mining unexpected associations for signalling potential adverse drug reactions from administrative health databases

Authors:
Huidong Jin;Jie Chen;Chris Kelman;Hongxing He;Damien McAullay;Christine M. O'Keefe
Affiliations:
CSIRO Health Informatics, Canberra, ACT, Australia;CSIRO Health Informatics, Canberra, ACT, Australia;NCEPH, The Australian National University, Canberra, ACT, Australia;CSIRO Health Informatics, Canberra, ACT, Australia;CSIRO Health Informatics, Canberra, ACT, Australia;CSIRO Health Informatics, Canberra, ACT, Australia
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

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. Due to their unexpectedness and infrequency, existing techniques cannot perform effectively. To handle this unexpectedness we introduce a new interestingness measure, unexpected-leverage, and give a user-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle infrequency, we develop a new algorithm, MUTARA, for mining simple UTARs. MUTARA effectively short-lists some known ADRs such as the disease esophagitis unexpectedly associated with the drug alendronate. Similarly, MUTARA signals atorvastatin followed by nizatidine or dicloxacillin which may be prescribed to treat its side effects stomach ulcer or urinary tract infection, respectively. Compared with association mining techniques, MUTARA signals potential ADRs more effectively.