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Mining multi-item drug adverse effect associations in spontaneous reporting systems
Rave Harpaz1, Herbert S Chase, Carol Friedman
1Department of Biomedical Informatics, Columbia University, 622 West 168th St, VC5, New York, NY 10032, USA. rave.harpaz@dbmi.columbia.edu
BMC Bioinformatics
|November 4, 2010
Summary
This study introduces a novel data mining approach to identify complex adverse drug event (ADE) associations from large datasets. The method successfully extracted numerous multi-drug adverse event relationships, validating existing ones and uncovering potential new drug interactions.
Area of Science:
- Pharmacovigilance
- Data Mining
- Drug Safety
Background:
- Current pharmacovigilance relies on bivariate analysis, studying single drug-adverse effect pairs.
- Detecting multi-item adverse drug event (ADE) associations involving multiple drugs and effects is crucial but challenging.
- Association rule mining, a data mining technique, is adapted for detecting complex ADE associations.
Purpose of the Study:
- To apply association rule mining to the FDA's AERS database for identifying multi-item ADE associations.
- To demonstrate the value of this tailored data mining method on a large scale.
- To explore the potential for discovering novel multi-drug adverse event relationships.
Main Methods:
- Utilized association rule mining, a data mining technique.
- Applied the method to the FDA's Adverse Event Reporting System (AERS) data.
- Analyzed 162,744 suspected ADE reports from 2008.
Main Results:
- Identified 1167 multi-item ADE associations.
- 67% of identified associations were clinically validated as previously recognized ADEs.
- Several potentially novel ADEs and 4% known drug-drug interactions were also identified.
Conclusions:
- Multi-item ADEs can be effectively extracted from the FDA AERS using the proposed methodology.
- The developed method serves as a valid approach for the initial identification of multi-item ADEs.
- Limitations related to the method and data quality were identified.
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