Abstract
The exploitation of clinical reports for generating alerts especially relies on the alignment of the dedicated terminologies, i.e., MedDRA (exploited in the pharmacovigilance area) and SNOMED International (exploited recently in France for encoding clinical documents). In this frame, we propose a cross-language approach for acquiring automatically alignments between terms from MedDRA and SNOMED International. We had the hypothesis that using additional languages could be helpful to complement the mappings obtained between French terms. Our approach is based on a lexical method for aligning MedDRA terms to those from SNOMED International. The concomitant use of multiple languages resulted in several hundreds of new alignments and successfully validated or disambiguated some of these alignments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lindberg, D.A., Humphreys, B.L., McCray, A.T.: The Unified Medical Language System. Methods Inf. Med. 32(4), 281–291 (1993)
Bodenreider, O., McCray, A.T.: Exploring semantic groups through visual approaches. J. Biomed. Inform. 36(6), 414–432 (2003)
Fung, K.W., Bodenreider, O.: Utilizing the UMLS for semantic mapping between terminologies. In: AMIA Annu. Symp. Proc., pp. 266–270 (2005)
Bodenreider, O.: Using SNOMED CT in combination with MedDRA for reporting signal detection and adverse drug reactions reporting. In: AMIA Annu. Symp. Proc., pp. 45–49 (2009)
Alecu, I., Bousquet, C., Mougin, F., Jaulent, M.-C.: Mapping of the WHO-ART terminology on SNOMED CT to improve grouping of related adverse drug reactions. Stud. Health Technol. Inform., 833–838 (2006)
Nadkarni, P.M., Darer, J.D.: Determining correspondences between high-frequency MedDRA concepts and SNOMED: A case study. BMC Med. Inform. Decis. Mak. 10, 66 (2010)
Mougin, F., Dupuch, M., Grabar, N.: Improving the mapping between medDRA and SNOMED CT. In: Peleg, M., Lavrač, N., Combi, C. (eds.) AIME 2011. LNCS, vol. 6747, pp. 220–224. Springer, Heidelberg (2011)
Malaisé, V., Isaac, A., Gazendam, L., Instituut, T., Brugman, H.: Anchoring dutch cultural heritage thesauri to WordNet: Two case studies. In: Proc. of the Workshop on Language Technology for Cultural Heritage Data, pp. 57–64 (2007)
Merabti, T., Soualmia, L.F., Grosjean, J., Palombi, O., Müller, J.-M., Darmoni, S.J.: Translating the foundational model of anatomy into French using knowledge-based and lexical methods. BMC Med. Inform. Decis. Mak. 11(1), 65 (2011)
Och, F.J., Ney, H.: Improved statistical alignment models. In: Proc. of the 38th Annual Meeting on Association for Computational Linguistics, pp. 440–447. Association for Computational Linguistics, Stroudsburg (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mougin, F., Grabar, N. (2013). Using a Cross-Language Approach to Acquire New Mappings between Two Biomedical Terminologies. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-38326-7_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38325-0
Online ISBN: 978-3-642-38326-7
eBook Packages: Computer ScienceComputer Science (R0)