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Published in:
2011 | OriginalPaper | Chapter
Mapping Orphanet Terminology to UMLS
Authors : Maja Miličić Brandt, Ana Rath, Andrew Devereau, Ségolène Aymé
Published in: Artificial Intelligence in Medicine
Publisher: Springer Berlin Heidelberg
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We present a method for creating mappings between the Orphanet terminology of rare diseases and the Unified Medical Language System (UMLS), in particular the SNOMED CT, MeSH, and MedDRA terminologies. Our method is based on: (i) aggressive normalisation of terms specific to the Orphanet terminology on top of standard UMLS normalisation; (ii) semantic ranking of partial candidate mappings in order to group similar mappings and attribute higher ranking to the more informative ones. Our results show that, by using the aggressive normalisation function, we increase the number of exact candidate mappings by 7.1-9.5% compared to a mapping method based on MetaMap. A manual assessment of our results shows a high precision of 94.6%. Our results imply that Orphanet diseases are under-represented in the aforementioned terminologies: SNOMED CT, MeSH, and MedDRA are found to contain only 35%, 42%, and 15% of the Orphanet rare diseases, respectively.