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Documentation in pharmacovigilance: using an ontology to extend and normalize Pubmed queries
Denis Delamarre1, Agnès Lillo-Le Louët, Laetitia Guillot
1INSERM U936, Université Rennes 1, IFR 140, Rennes, France. Denis.Delamarre@chu-rennes.fr
Studies in Health Technology and Informatics
|September 16, 2010
Summary
An ontology-based search tool significantly improves recall for identifying adverse drug reactions (ADRs) in pharmacovigilance databases. Further methods are required to enhance precision in ADR detection.
Area of Science:
- Pharmacovigilance and Drug Safety
- Biomedical Informatics
- Medical Ontology and Terminology
Background:
- Systematic reviews of databases like PubMed are crucial for understanding adverse drug reactions (ADRs).
- Efficiently querying these databases for ADR-drug relationships is a key challenge in pharmacovigilance.
- Existing search methods may lack the specificity and comprehensiveness needed for thorough ADR identification.
Purpose of the Study:
- To compare four distinct approaches for querying PubMed using ADR-drug terms.
- To evaluate the impact of an adverse effects ontology on normalizing and extending search queries.
- To assess the potential of an ontology-based tool to improve ADR detection in pharmacovigilance.
Main Methods:
- Four query methods were compared: direct PubMed search, normalized query with MeSH terms, extended MeSH hierarchy search, and an ontology-based (OntoEIM) MedDRA term grouping search.
- The OntoEIM resource integrates MedDRA terminology and contains over 58,000 classes.
- Sixteen queries were performed, with relevant publications manually selected by two pharmacovigilance experts.
Main Results:
- The ontology-based method achieved the highest recall (74%), outperforming direct search (63%), normalized query (50%), and extended MeSH query (67%).
- The ontology-based approach successfully retrieved relevant publications in 4 out of 16 cases where other methods yielded no results.
- Precision varied across methods, with the ontology-based approach yielding 4% precision, indicating a trade-off with recall.
Conclusions:
- An ontology-based search strategy demonstrably enhances recall for identifying ADRs in literature.
- While recall is improved, further development is necessary to increase the precision of ontology-driven searches.
- Ontology-based tools represent a promising advancement for pharmacovigilance but require complementary methods for optimal performance.
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