Marie Dupuch

Improving the mapping between MedDRA and SNOMED CT

MedDRA is exploited for the indexing of pharmacovigilance spontaneous reports. But since spontaneous reports cover only a small proportion of the existing adverse drug reactions, the exploration of clinical reports is seriously considered. Through the UMLS, the current mapping between MedDRA and SNOMED CT, this last being used for indexing clinical data in many countries, is only 42%. In this work, we propose to improve this mapping through an automatic lexical-based approach. We obtained 308 direct mappings of a MedDRA term to a SNOMED CT concept. After segmenting MedDRA terms, we identified 535 full mappings associating a MedDRA term with one or more SNOMED CT concepts. The direct approach resulted in 199 (64.6%) correct mappings while through segmentation this number raises to 423 (79.1%). On the whole, our method provided interesting and useful results.

Automatic creation and refinement of the clusters of pharmacovigilance terms

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. The detection of adverse drug reactions is performed thanks to statistical algorithms and to groupings of ADR terms. Standardized MedDRA Queries (SMQs) are the groupings which become a standard for assisting the retrieval and evaluation of MedDRA-coded ADR reports all through the world. Currently 84 SMQs have been created manually by experts, while several important safety topics are not yet covered. Dependent on the context of their application, these SMQs show a high degree of sensitivity and often appear to be over-inclusive. For pharmacovigilance experts it represents an important and tedious filtering of data. The objective of this work is to propose an automatic method for assisting the creation of SMQs and also for the refinement of their organization further to the creation of smaller clusters of ADR terms. In this work we propose to exploit the semantic distance and clustering approaches. We perform several experiments and vary several parameters of the method.

Exploitation of semantic methods to cluster pharmacovigilance terms

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. This activity is usually performed within dedicated databases (national, European, international...), in which the ADRs declared for patients are usually coded with a specific controlled terminology MedDRA (Medical Dictionary for Drug Regulatory Activities). Traditionally, the detection of adverse drug reactions is performed with data mining algorithms, while more recently the groupings of close ADR terms are also being exploited. The Standardized MedDRA Queries (SMQs) have become a standard in pharmacovigilance. They are created manually by international boards of experts with the objective to group together the MedDRA terms related to a given safety topic. Within the MedDRA version 13, 84 SMQs exist, although several important safety topics are not yet covered. The objective of our work is to propose an automatic method for assisting the creation of SMQs using the clustering of semantically close MedDRA terms. The experimented method relies on semantic approaches: semantic distance and similarity algorithms, terminology structuring methods and term clustering. The obtained results indicate that the proposed unsupervised methods appear to be complementary for this task, they can generate subsets of the existing SMQs and make this process systematic and less time consuming.

Exploitation de la distance sémantique pour la création de groupements de termes en pharmacovigilance

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. Beside other methods, statistical methods are used to detect new ADRs in the framework of signal detection. Groupings of terms containing similar ADRs allow to increase the signal intensity and to improve the detection of new ADRs. SMQs have become reference groupings in the field of pharmacovigilance. They are built on the MedDRA terminology and thanks to the study of scientific literature. Even if SMQs are built manually by experts, they still show some shortcomings: they tend to be over-inclusive and thus become too sensitive, although they can also miss several relevant terms. Moreover, the spectrum of available SMQs is limited. The objective of this work is to propose an automated method for a flexible creation of groupings of terms. This method is based on exploitation of the semantic distance between MedDRA terms. In a first experience, we used ARD terms alone and obtained results with a high precision (mean 74% within the interval [49; 91]). In a second experience, we used ADR terms and their formal definitions and this worsened the results because the semantic information within definitions may be missing. We assume that more exhaustive definitions will have a positive effect on results.