Improving the mapping between MedDRA and SNOMED CT

Authors:
Fleur Mougin;Marie Dupuch;Natalia Grabar
Affiliations:
LESIM, INSERM, ISPED, University Bordeaux Segalen, France;Centre de Recherche des Cordeliers, Université Pierre et Marie Curie - Paris6, INSERM, Paris, France;STL, CNRS, UMR, Université Lille 3, France
Venue:
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Year:
2011

Citing 2
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Exploring semantic groups through visual approaches

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A review of auditing methods applied to the content of controlled biomedical terminologies

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Automatic creation and refinement of the clusters of pharmacovigilance terms

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

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Abstract

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.