Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties.

Marie-Christine Jaulent,Julien Souvignet,Cédric Bousquet,Éric Sadou,Gunnar Declerck

doi:10.3389/fphar.2019.00975

Abstract

Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (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 semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System 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 term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 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 has clinical course cyclic). Results: The Unified Medical Language System 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 1,935 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.

Highlights

“Once the Nadkarni and Darer’s mapping propositions were validated, modified or completed, we applied the same procedure as described in the section Using Medical Dictionary for Regulatory Activities (MedDRA)-to-SNOMED SNOMED Clinical Terms (CT) Mappings From Unified Medical Language System (UMLS) Metathesaurus to pick up information from SNOMED CT and define the MedDRA concepts of OntoADR
Using the set of SNOMED CT relations available in OntoADR, we realized manually the definition of those MedDRA terms (53 in total) for which no mapping could be found by Nadkarni and Darer
The use, after verification and eventually correction and complementation, of mappings proposed by Nadkarni and Darer, allowed us to complete the definition of 786 supplementary MedDRA preferred term (PT) in OntoADR

Summary

Introduction

Formal representation of semantics as provided by computational ontologies and associated semantic Web techniques have been extensively used in medical data integration systems in the last decade (Sheth et al, 2005), and they tend to be acknowledged as a powerful means to improve the quality of the processing chain of medical data, process automatic extraction of information and knowledge from large databases or ensure semantic interoperability between disparate data processing systems (Park and Hardiker, 2009; Schriml et al, 2012; Schulz and Jansen, 2013).In the medical domain, classic terminologies are gradually giving way to clinical terminologies, in which terms are defined using knowledge representation languages (Rossi Mori et al, 1998). It is frequently difficult to identify the exact MedDRA category that represents a given medical condition under investigation in a sufficiently specific and exhaustive way, for example, during a pharmacovigilance database search (Brown, 2003). The standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. As HLT within a SOC constitutes disjoint classes, it is seldom reliable to consider only one HLT or higher level category when searching for MedDRA terms related to a pharmacovigilance safety topic (Bousquet et al, 2005; Asfari et al, 2016). Identifying clinically related terms in MedDRA is not an easy task, as those terms might exist in different locations of the MedDRA hierarchy

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