Mining Unexpected Associations for Signalling Potential Adverse Drug Reactions from Administrative Health Databases

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 in-frequency, 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 di-cloxacillin 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.