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Modeling, building and evaluating an ontology for the automatic characterization of adverse drug effects during 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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https://hal.inria.fr/hal-00845365
Contributor : Lina F Soualmia Connect in order to contact the contributor
Submitted on : Wednesday, July 17, 2013 - 1:00:02 AM
Last modification on : Wednesday, March 2, 2022 - 10:10:08 AM

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

Citation

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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