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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Pharmacol. | doi: 10.3389/fphar.2019.00975

Ontological and non-ontological resources for associating MedDRA terms to SNOMED CT concepts with semantic properties

 Cedric Bousquet1, 2*, Julien Souvignet1, Eric Sadou1, Marie-Christine Jaulent1 and  Gunnar Declerck3
  • 1INSERM U1142 Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, France
  • 2Unit of Public Health and Medical Informatics, Université Jean Monnet, France
  • 3EA 2223 Costech Connaissance, Organisation et Systèmes Techniques, Université de Technologie de Compiègne, France

Background: Formal definitions allow selecting terms (e.g., identifying all terms related to ‘Infectious disease’ using the query ‘hasCausativeAgent Organism’), and terminological reasoning (e.g., ‘Hepatitis B’ is a ‘Hepatitis’, and is an ‘Infectious disease’). However, the standard international terminology MedDRA used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA.
Methods: We developed several methods that collectively allow a semi-automatic semantic enrichment of MedDRA: (1) using MedDRA-to-SNOMED CT mappings (available in the UMLS metathesaurus or other mapping resources, e.g. the MedDRA preferred term ‘Hepatitis B’ is associated to the SNOMED CT concept ‘Type B viral hepatitis’) to extract terms definitions (e.g., ‘Hepatitis B’ is associated with the following properties: hasFindingSite LiverStructure, hasAssociatedMorphology InflammationMorphology, and hasCausativeAgent HepatitisBvirus); (2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “Blood and lymphatic system disorders” is decomposed in Blood system disorders AND Lymphatic system disorders), or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “Cyclic neutropenia” leads to the property hasClinicalCourse Cyclic).
Results: The UMLS Metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010, and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time.
Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The Ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.

Keywords: Adverse Drug Reaction, MedDRA, SNOMED CT, ontology, Pharmacovigilance

Received: 18 Jul 2018; Accepted: 31 Jul 2019.

Edited by:

Lixia Yao, Mayo Clinic, United States

Reviewed by:

Yongqun Oliver He, University of Michigan Health System, United States
Zhe He, Florida State University, United States  

Copyright: © 2019 Bousquet, Souvignet, Sadou, Jaulent and Declerck. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Cedric Bousquet, INSERM U1142 Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Paris, F-75006, France,