Pharmacovigilance is “the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problems” [1
]. An adverse drug reaction (ADR) is defined as “An appreciably harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product, which predicts hazard from future administration and warrants prevention or specific treatment, or alteration of the dosage regimen, or withdrawal of the product [2
].” WHO-ART (World Health Organisation – Adverse Reaction Terminology) [3
] and MedDRA (Medical Dictionary for Drug Regulatory Activities) [4
] are the terminologies used in pharmacovigilance for coding of ADRs and data statistical analysis. They are declared as mandatory by the national and international regulatory authorities.
WHO-ART and MedDRA are built on the model of traditional terminologies like the International Classification of diseases. Therefore there are no formal definitions available to constrain the meaning of terms. We showed that terminological reasoning improves the performances of both data mining [5
] and data access [6
] in pharmacovigilance databases.
As a first step we explored the manual construction of an ontology of ADR terms [7
]. Then we tried several approaches for knowledge extraction using natural language processing techniques and/or mapping to the UMLS. While using the UMLS we considered mappings to both: Snomed international [8
] and SNOMED-CT [9
]. Mapping to SNOMED international was based on the SNOMED axes. In order to constrain the definitions obtained from SNOMED-CT we filtered the kinds of relations that were the most relevant for the study domain. Whereas the manual approach was very precise and time consuming, the knowledge extraction, albeit on a poor ontological basis, was very relevant to lowering the development costs.
Before we continue the development of the formal definitions, we need to ensure a consistent ontological representation that not only takes into account the minimal requirements for the representation of ADRs but also has a simple structure in order to avoid unnecessary complexity and time consumption like in the manual approach.
The European Standard Body CEN TC 251 WG2 (Comité Européen de Normalisation Technical Committee 251 Working Group 2) and later the International Standard Organisation ISO TC 215 WG3 elaborated and developed a standard approach for biomedical terminology named Categorial structure [10
In order to build a road towards standardisation the European Standard Body CEN has stated that it was not possible to convince the different European member states using different national languages to agree on a reference clinical terminology or to standardise a detailed language independent biomedical ontology. The two main supportive arguments were that European countries speak different natural languages and that different health care professionals within the same natural language do not convey the same meaning through a particular terminology. CEN has developed since 1990 the categorial structure as a step standardising only the terminologies model structure. Indeed the standards needed to facilitate developments in biomedical terminologies and not prevent the quickly evolving volume of terms used for different goals.
The CEN Categorial structure is defined as a minimal set of health care domain constraints to represent a biomedical terminology in a precise healthcare domain for example surgical procedures [11
], terminologies of human anatomy [12
], or properties in laboratory medicine [13
]. These constraints consist of 1) a list of semantic categories; 2) the goal of the Categorial structure; 3) the list of semantic links between semantic categories authorised with their associated semantic categories; 4) the minimal constraints allowing the generation and validation of well formed terminological phrases.
This work was limited to the study of WHO-ART. Our objective was to describe a Categorial structure for ADRs. In the next section we describe our set of terms and methodology of development of a Categorial structure. In the third section we describe the semantic categories and relations we identified. Finally we discuss limits of this work and future perspectives.