Abstract
This chapter presents the different medical classifications and terminologies as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, ATC etc.
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References
Ahltorp, M., Skeppstedt, M., Kitajima, S., Rzepka, R., & Araki, K. (2014). Medical vocabulary mining using distributional semantics on Japanese patient blogs. In 6th International Symposium on Semantic Mining in Biomedicine (SMBM), Aveiro, Portugal, October 6–7, 2014 (pp. 57–62).
Alfalahi, A., Skeppstedt, M., Ahlbom, R., Baskalayci, R., Henriksson, A., Asker, L., et al. (2015). Expanding a dictionary of marker words for uncertainty and negation using distributional semantics. In Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis, Louhi, Held in Conjunction with EMNLP 2015, Lisbon, Portugal (pp. 90–96). Association for Computational Linguistics.
Chen, R., & Klein, G. (2007). The openEHR Java reference implementation project. Studies in Health Technology and Informatics, 129(1), 58.
Chen, R., Klein, G. O., Sundvall, E., Karlsson, D., & Åhlfeldt, H. (2009). Archetype-based conversion of EHR content models: Pilot experience with a regional EHR system. BMC Medical Informatics and Decision Making, 9(1), 33.
Henriksson, A., Moen, H., Skeppstedt, M., Daudaravicius, V., & Duneld, M. (2014). Synonym extraction and abbreviation expansion with ensembles of semantic spaces. Journal of Biomedical Semantics, 5, 6.
Humphreys, B. L., Lindberg, D. A. B., Schoolman, H. M., & Barnett, G. O. (1998). The unified medical language system. Journal of the American Medical Informatics Association, 5(1), 1–11.
IHTSDO. (2016). SNOMED-CT, Systematized nomenclature of medicine-clinical terms. http://www.ihtsdo.org/snomed-ct/. Accessed 11 Jan 2018.
Liu, H., Aronson, A. R., & Friedman, C. (2002). A study of abbreviations in medline abstracts. In AMIA Annual Symposium Proceedings (p. 464). American Medical Informatics Association.
Moriyama, I. M., Loy, R. M., Robb-Smith, A. H., Rosenberg, H. M., & Hoyert, D. L. (2011). History of the Statistical Classification of Diseases and Causes of Death. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.
Skeppstedt, M., Kvist, M., & Dalianis, H. (2012). Rule-based entity recognition and coverage of SNOMED CT in Swedish clinical text. In Proceedings of the Eighth International Conference on Language Resources and Evaluation, LREC 2012 (pp. 1250–1257).
Suominen, H., Salanterä, S., Velupillai, S., Chapman, W. W., Savova, G., Elhadad, N., et al. (2013). Overview of the ShARe/CLEF eHealth Evaluation Lab 2013. In Information Access Evaluation. Multilinguality, Multimodality, and Visualization (pp. 212–231). Berlin: Springer.
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Dalianis, H. (2018). Medical Classifications and Terminologies. In: Clinical Text Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-78503-5_5
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DOI: https://doi.org/10.1007/978-3-319-78503-5_5
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