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1. Documentation in pharmacovigilance: using an ontology to extend and normalize Pubmed queries

Author

Delamarre, Denis, Lillo-Le Louët, Agnès, Guillot, Laetitia, Jamet, Anne, Sadou, Eric, Ouazine, Theo, Burgun, Anita, Jaulent, Marie-Christine, Modélisation Conceptuelle des Connaissances Biomédicales, Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Centre Régional de Pharmacovigilance ( CRPV ), Assistance publique - Hôpitaux de Paris (AP-HP)-Hôpital Européen Georges Pompidou [APHP] ( HEGP ), Laboratoire de Santé Publique et Informatique Médicale ( SPIM ), Institut National de la Santé et de la Recherche Médicale ( INSERM ), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Centre Régional de Pharmacovigilance (CRPV), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Laboratoire de Santé Publique et Informatique Médicale (SPIM), Institut National de la Santé et de la Recherche Médicale (INSERM), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)

Subjects
MESH: Terminology as Topic, PubMed, Drug-Related Side Effects and Adverse Reactions, Adverse drug reaction reporting, Adverse drug reaction, MESH: Documentation, Documentation, MESH: Drug Toxicity, MESH: Natural Language Processing, MESH : Database Management Systems, Terminology as Topic, MESH : Vocabulary, Controlled, Data Mining, Humans, Information retrieval, [ SDV.IB ] Life Sciences [q-bio]/Bioengineering, Natural Language Processing, MESH: Humans, Ontology, MESH : Data Mining, MESH: Data Mining, MESH : Humans, MESH : Drug Toxicity, MESH: PubMed, MESH: Vocabulary, Controlled, MESH : Natural Language Processing, MESH : PubMed, MESH : Terminology as Topic, Vocabulary, Controlled, Database Management Systems, [SDV.IB]Life Sciences [q-bio]/Bioengineering, MESH : Documentation, Databases bibliographic, MESH: Database Management Systems
Abstract

International audience; 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.

Published
2010
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2. Documentation in Pharmacovigilance: Using an ontology to extend and normalize Pubmed queries.

Author

Safran, C., Reti, S., Marin, H.F., Delamarre, Denis, Lillo-Le Louët, Agnès, Guillot, Laetitia, Jamet, Anne, Sadou, Eric, Ouazine, Theo, Burgun, Anita, and Jaulent, Marie-Christine

Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2010

3. Documentation in pharmacovigilance: using an ontology to extend and normalize Pubmed queries.

Author

Delamarre D, Lillo-Le Louët A, Guillot L, Jamet A, Sadou E, Ouazine T, Burgun A, and Jaulent MC

Subjects
Humans, Vocabulary, Controlled, Data Mining methods, Database Management Systems, Documentation methods, Drug-Related Side Effects and Adverse Reactions epidemiology, Natural Language Processing, PubMed, Terminology as Topic
Abstract

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.

Published
2010

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