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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Improving the Mapping between MedDRA and SNOMED CT
verfasst von : Fleur Mougin, Marie Dupuch, Natalia Grabar
Erschienen in: Artificial Intelligence in Medicine
Verlag: Springer Berlin Heidelberg
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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.