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Towards a UMLS-Integratable Vietnamese Medical Terminology

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1215))

Abstract

Lexical resources play an essential role in text processing. In this paper, we present our work on the construction of a Vietnamese medical terminology integratable into the UMLS multilingual metathesaurus (Unified Medical Language System). The construction of the Vietnamese medical terminology is done by collecting terms from existing lexical sources on one hand, and by extracting terms from Vietnamese medical corpora on the other. In order to draw maximum benefit from the varied sources and corpora that can be collected, we have developed a set of tools adapted to the specificities of each of those resources, based upon proven techniques. This allows us to acquire consequent amounts of good quality mono- and bilingual medical terminology data.

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Notes

  1. 1.

    http://ctakes.apache.org/.

  2. 2.

    https://metamap.nlm.nih.gov/.

  3. 3.

    https://www.i2b2.org/.

  4. 4.

    https://www.nlm.nih.gov/research/umls/.

  5. 5.

    https://en.wikipedia.org/wiki/Crowdsourcing.

  6. 6.

    http://123.31.27.68/ICD/ICD10.htm.

  7. 7.

    https://www.wikipedia.org/.

  8. 8.

    https://vi.wikipedia.org/wiki/Thuy_dau.

References

  1. Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, D267–D270 (2004). https://doi.org/10.1093/nar/gkh061

    Article  Google Scholar 

  2. Bond, F., Chang, Z., Uchimoto, K.: Extracting bilingual terms from mainly monolingual data. In: 14th Annual Meeting of the Association for Natural Language Processing, Tokyo, Japan (2008)

    Google Scholar 

  3. Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990). https://www.aclweb.org/anthology/J90-1003

    Google Scholar 

  4. Conrado, M., Pardo, T., Rezende, S.: A machine learning approach to automatic term extraction using a rich feature set. In: Proceedings of the 2013 NAACL HLT Student Research Workshop, pp. 16–23. Association for Computational Linguistics, Atlanta, Georgia (June 2013). https://www.aclweb.org/anthology/N13-2003

  5. Daille, B.: Conceptual structuring through term variations. In: Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition, and Treatment, no. 1, pp. 9–16 (2003)

    Google Scholar 

  6. Demner-Fushman, D., Rogers, W., Aronson, A.: Metamap lite: an evaluation of a new java implementation of metamap. J. Am. Med. Inform. Assoc.: JAMIA 24, 841–844 (2017). https://doi.org/10.1093/jamia/ocw177

    Article  Google Scholar 

  7. Forrey, A.W., et al.: Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results. Clin. Chem. 42(1), 81–90 (1996)

    Article  Google Scholar 

  8. Franzi, K., Ananiadou, S.: The C/NC value domain independent method for multi-word term extraction. J. Nat. Lang. Process. 6, 145–180 (1999)

    Article  Google Scholar 

  9. Friedman, C.: Towards a comprehensive medical language processing system: methods and issues. In: Proceedings: A Conference of the American Medical Informatics Association/AMIA Annual Fall Symposium. AMIA Fall Symposium, vol. 4, pp. 595–599 (February 1997)

    Google Scholar 

  10. Hliaoutakis, A., Zervanou, K., Petrakis, E.: The AMTEx approach in the medical document indexing and retrieval application. Data Knowl. Eng. 68, 380–392 (2009). https://doi.org/10.1016/j.datak.2008.11.002

    Article  Google Scholar 

  11. Justeson, J.S., Katz, S.M.: Technical terminology: some linguistic properties and an algorithm for identification in text. Nat. Lang. Eng. 1(1), 9–27 (1995). https://doi.org/10.1017/S1351324900000048

    Article  Google Scholar 

  12. Karën, F., Gilles, A., Bretonnel, C.K.: Amazon mechanical turk: gold mine or coal mine? Comput. Linguist. 37(2), 413–420 (2011). https://doi.org/10.1162/COLI_a_00057

    Article  Google Scholar 

  13. Keating, M., Furberg, R.D.: A methodological framework for crowdsourcing in research. In: Proceedings of the 2013 Federal Committee on Statistical Methodology (FCSM) Research Conference (2013)

    Google Scholar 

  14. Korkontzelos, I., Klapaftis, I.P., Manandhar, S.: Reviewing and evaluating automatic term recognition techniques. In: Nordström, B., Ranta, A. (eds.) GoTAL 2008. LNCS (LNAI), vol. 5221, pp. 248–259. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85287-2_24

    Chapter  Google Scholar 

  15. Lafourcade, M.: Making people play for lexical acquisition with the JeuxDeMots prototype. In: 7th International Symposium on Natural Language Processing, SNLP 2007, Pattaya, Chonburi, Thailand, p. 7 (December 2007). https://hal-lirmm.ccsd.cnrs.fr/lirmm-00200883

  16. Li, Y., et al.: Extracting medical knowledge from crowdsourced question answering website. IEEE Trans. Big Data 6, 1–1 (2016). https://doi.org/10.1109/TBDATA.2016.2612236

    Article  Google Scholar 

  17. Liu, J., Morin, E., Saldarriaga, S.P.: Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms. In: Proceedings of the 27th International Conference on Computational Linguistics (COLING) (2018)

    Google Scholar 

  18. Macken, L., Lefever, E., Hoste, V.: TExSIS: bilingual terminology extraction from parallel corpora using chunk-based alignment. Terminology 19, 1–30 (2013). https://doi.org/10.1075/term.19.1.01mac

    Article  Google Scholar 

  19. Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. Int. J. Artif. Intell. Tools 13, 157–169 (2003)

    Article  Google Scholar 

  20. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, vol. 2, pp. 3111–3119. Curran Associates Inc., USA (2013). http://dl.acm.org/citation.cfm?id=2999792.2999959

  21. Organization, W.H.: International Statistical Classification of Diseases and Related Health Problems. Tenth Revision, vol. 2 (2010). https://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf

  22. Paniagua, J., Korzynski, P.: Social Media Crowdsourcing, pp. 1–5. Springer, New York, New York, NY (2017)

    Google Scholar 

  23. Patrick, R., Julien, G., Christian, L., Antoine, G.: Automatic medical encoding with SNOMED categories. BMC Med. Inform. Decis. Mak. 8, S6 (2008)

    Article  Google Scholar 

  24. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988). https://doi.org/10.1016/0306-4573(88)90021-0

    Article  Google Scholar 

  25. Savova, G., et al.: Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc.: JAMIA 17, 507–13 (2010). https://doi.org/10.1136/jamia.2009.001560

    Article  Google Scholar 

  26. Soysal, E., et al.: CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines. J. Am. Med. Inform. Assoc. 25, ocx132 (2017). https://doi.org/10.1093/jamia/ocx132

    Article  Google Scholar 

  27. Trieu, N.Q., Song, P.: Medical Encyclopedia of Vietnam. Medical Publishing House One Member Company Limited, Ha Noi (2011)

    Google Scholar 

  28. Wang, A., Hoang, C.D.V., Kan, M.Y.: Perspectives on crowdsourcing annotations for natural language processing. Lang. Resour. Eval. 47(1), 9–31 (2013)

    Article  Google Scholar 

  29. Yang, W., Yan, J., Lepage, Y.: Extraction of bilingual technical terms for Chinese-Japanese patent translation. In: Proceedings of the NAACL Student Research Workshop, pp. 81–87. Association for Computational Linguistics, San Diego (June 2016). https://doi.org/10.18653/v1/N16-2012. https://www.aclweb.org/anthology/N16-2012

  30. Zhang, Z., Iria, J., Brewster, C., Ciravegna, F.: A comparative evaluation of term recognition algorithms. In: LREC 2008 (2008). http://www.lrec-conf.org/proceedings/lrec2008/pdf/538_paper.pdf

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Correspondence to The Quyen Ngo .

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Ngo, T.Q., Ha, M.L., Nguyen, T.M.H., Hoang, T.M.H., Nguyen, V.H. (2020). Towards a UMLS-Integratable Vietnamese Medical Terminology. In: Nguyen, LM., Phan, XH., Hasida, K., Tojo, S. (eds) Computational Linguistics. PACLING 2019. Communications in Computer and Information Science, vol 1215. Springer, Singapore. https://doi.org/10.1007/978-981-15-6168-9_32

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  • DOI: https://doi.org/10.1007/978-981-15-6168-9_32

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