Formalizing MedDRA to support semantic reasoning on adverse drug reaction terms

Area of Science:

• Pharmacovigilance and Drug Safety
• Medical Informatics and Ontologies
• Computational Linguistics and Terminology

Background:

• The Medical Dictionary for Regulatory Activities (MedDRA) is crucial for coding adverse drug reactions (ADRs) and signal detection.
• MedDRA's hierarchical structure limits flexible searching and the discovery of novel adverse event categories.
• Integrating formal definitions can enhance MedDRA's utility for semantic reasoning and data mining.

Purpose of the Study:

• To develop an "ontologized" version of MedDRA, named OntoADR, by associating formal definitions with its terms.
• To enable semantic reasoning and overcome the constraints of MedDRA's predefined categories.
• To facilitate the retrieval of terms based on case definitions, potentially revealing novel categories.

Main Methods:

• Converted MedDRA's five-level hierarchy into a subsumption tree.
• Developed formal definitions for MedDRA terms using multiple approaches: mapping to SNOMED-CT, semi-automatic algorithms, and manual curation.
• Created an OWL (Web Ontology Language) file, OntoADR, to store the formalized MedDRA terms and definitions.

Main Results:

• Successfully created OntoADR, an "ontologized" version of MedDRA with associated formal definitions.
• Demonstrated a method for enhancing MedDRA's terminological organization through formal semantics.
• Identified challenges and limitations inherent in the process of ontologizing MedDRA.

Conclusions:

• The OntoADR project represents a significant step towards enabling semantic reasoning with MedDRA.
• Formal definitions enhance MedDRA's capability for data mining and adverse drug reaction signal discovery beyond its inherent structure.
• Further development is needed to address the identified problems and limitations in creating a comprehensive ontologized MedDRA.