Documentation in pharmacovigilance: using an ontology to extend and normalize Pubmed queries.
OBJECTIVE
To assess and understand adverse drug reactions (ADRs), a systematic review of reference databases like Pubmed is a necessary and mandatory step in Pharmacovigilance. In order to assist pharmacovigilance team with a computerized tool, we performed a comparative study of 4 different approaches to query Pubmed through ADR-drug terms. The aim of this study is to assess how an ontology of adverse effects, used to normalize and extend queries, could improve this search.


METHODS
The ontological resource OntoEIM contains 58,000 classes and integrates MedDRA terminology. The entry point is a ADR-Drug term and the four methods are (i) a direct search on Pubmed (ii) a search with a normalized query enhanced with domain-specific Mesh Heading criteria, (iii) a search with the same elaborated query extended to the MeSH sub-hierarchy of the adverse effect entry and (iv) a search with a set of MedDRA terms grouped by subsomption in the OntoEIM ontology. For each of the 16 queries performed and analysed, relevant publications are selected "manually" by two pharmacovigilant experts.


RESULTS
The recall is respectively of 63%, 50%, 67% and 74%, the precision of 13%, 26%, 29% and 4%. The best recall is provided by the ontology-based method, for 4 cases out of 16 this method returns relevant publications when the others return no results.


CONCLUSIONS
Results show that an ontology-based search tool improves the recall performance, but other tools and methods are needed to raise the precision.
Delamarre
Denis
D
INSERM U936, Université Rennes 1, IFR 140, Rennes, France. Denis.Delamarre@chu-rennes.fr
Lillo-Le Louët
Agnès
A
Guillot
Laetitia
L
Jamet
Anne
A
Sadou
Eric
E
Ouazine
Theo
T
Burgun
Anita
A
Jaulent
Marie-Christine
MC
D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems; D004282:Documentation / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D006801:Humans; D009323:Natural Language Processing; D039781:PubMed; D009626:Terminology as Topic; D018875:Vocabulary, Controlled