OBJECTIVES: 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. MATERIAL AND METHOD: 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. CONCLUSION: Results show that an ontology-based search tool improves the recall performance, but other tools and methods are needed to raise the precision.
OBJECTIVES: 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. MATERIAL AND METHOD: 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. CONCLUSION: Results show that an ontology-based search tool improves the recall performance, but other tools and methods are needed to raise the precision.
Authors: Paul Avillach; Jean-Charles Dufour; Gayo Diallo; Francesco Salvo; Michel Joubert; Frantz Thiessard; Fleur Mougin; Gianluca Trifirò; Annie Fourrier-Réglat; Antoine Pariente; Marius Fieschi Journal: J Am Med Inform Assoc Date: 2012-11-29 Impact factor: 4.497
Authors: Tomasz Adamusiak; Tony Burdett; Natalja Kurbatova; K Joeri van der Velde; Niran Abeygunawardena; Despoina Antonakaki; Misha Kapushesky; Helen Parkinson; Morris A Swertz Journal: BMC Bioinformatics Date: 2011-05-29 Impact factor: 3.307