Modeling, building and evaluating an ontology for the automatic characterization of adverse drug effects during pharmacovigilance.

Catherine Duclos 1 Lina F Soualmia 2 Sonia Krivine 1 Anne Jamet 3 Agnes Lillo-Louët
1 LIM&BIO - Laboratoire d'Informatique Médicale et de BIOinformatique
2 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
3 CRPV - Centre Régional de Pharmacovigilance
Abstract : BACKGROUND: The characterization of spontaneous reported cases is fundamental for pharmacovigilance. This task is time consuming and its reproducibility is low. OBJECTIVE: To develop a system founded on an ontology that automatically instantiates spontaneous reported cases as "known" adverse drug effects (ADE) only if the reported ADEs are described in drug compendia. METHODS: A simple ontology of drugs and their related adverse effects represented in description logics was developed from a drug database. Manual evaluation was carried out on 378 spontaneous reported cases instantiated as "known ADE of a chemical class". The initial manual characterization was reviewed by a pharmacovigilance expert to validate the generated automatic characterization. RESULTS: The ontology is composed by 57,04 concepts and 5 relations. It was successfully validated thanks to Pellet reasoner and it contains neither inconsistencies nor cycles. In this validation, 86% of the instantiated spontaneous reported cases effectively concerned notorious ADEs, whereas only 75% were initially identified manually as related to notorious ADEs. CONCLUSION: This system can assist characterization by applying a reasoning process similar to that used by experts in the search for ADEs.
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Article dans une revue
Studies in Health Technology and Informatics, IOS Press, 2010, 160 (Pt 2), pp.1005-9
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https://hal.inria.fr/hal-00845365
Contributeur : Lina F Soualmia <>
Soumis le : mercredi 17 juillet 2013 - 01:00:02
Dernière modification le : mardi 5 juin 2018 - 10:14:25

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  • HAL Id : hal-00845365, version 1
  • PUBMED : 20841835

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LITIS | COMUE-NORMANDIE | UNIV-PARIS13 | USPC | INSA-ROUEN | UNIROUEN

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Catherine Duclos, Lina F Soualmia, Sonia Krivine, Anne Jamet, Agnes Lillo-Louët. Modeling, building and evaluating an ontology for the automatic characterization of adverse drug effects during pharmacovigilance.. Studies in Health Technology and Informatics, IOS Press, 2010, 160 (Pt 2), pp.1005-9. 〈hal-00845365〉

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