Towards Cross Language Morphologic Negation Identification in Electronic Health Records

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 141)


The current paper presents an approach for analyzing the Electronic Health Records (EHRs) with the goal of automatically identifying morphologic negation such that swapping the truth values of concepts introduced by negation does not interfere with understanding the medical discourse. To identify morphologic negation we propose the RoPreNex strategy that represents the adaptation of our PreNex approach to the Romanian language [1]. We evaluate our proposed solution on the MTsamples [2] dataset. The results we obtained are promising and ensure a reliable negation identification approach in medical documents. We report precision of 92.62 % and recall of 93.60 % in case of the morphologic negation identification for the source language and an overall performance in the morphologic negation identification of 77.78 % precision and 80.77 % recall in case of the target language.


Cross language Morphologic negation Electronic health records Dictionary 



The authors would like to acknowledge the contribution of the COST Action IC1303 - AAPELE.


  1. 1.
    Barbantan, I., Potolea, R.: Exploiting Word Meaning for Negation Identification in Electronic Health Records, IEEE AQTR (2014)Google Scholar
  2. 2.
    MTsamples: Transcribed Medical Transcription Sample Reports and Examples. Last accessed on 23.10, 2012Google Scholar
  3. 3.
    Iordachioaia, G., Richter, F.: Negative concord in Romanian as polyadic quantification. In: Muller, S. (ed.) Proceedings of the 16th International Conference on Head-Driven Phrase Structure Grammar Georg-August-Universitat Gottingen, pp. 150–170. CSLI Publications, Germany (2009)Google Scholar
  4. 4.
    Givon, T.: English Grammar: A Function-Based Introduction. Benjamins, Amsterdam, NL (1993)CrossRefGoogle Scholar
  5. 5.
    Orasan, C., Chiorean, O.A.: Evaluation of a cross–lingual romanian–english multi–document summariser. In: Proceedings of LREC 2008 Conference, Marrakech, Morocco (2008)Google Scholar
  6. 6.
    Chapman, W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanan, B.G.: A simple algorithm for identifying negated findings and diseases in discharge summaries. J. Biomed. Inform. 34(5), 301–310 (2001)CrossRefGoogle Scholar
  7. 7.
    Mutalik, P.G., Deshpande, A., Nadkarni, P.M.: Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J. Am. Med. Inform. Assoc. 8(6), 80–91 (2001)CrossRefGoogle Scholar
  8. 8.
    Vincze, V., Szarvas, G., Farkas, R., Móra, R., Csirik, J.: The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. In: Natural Language Processing in Biomedicine (BioNLP) ACL Workshop Columbus, OH, USA (2008)Google Scholar
  9. 9.
    Blanco, E., Moldovan, D.: Some issues on detecting negation from text. In: Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference (2011)Google Scholar
  10. 10.
    Councill, I.G., McDonald, R., Velikovich, L.: What’s great and what’s not: learning to classify the scope of negation for improved sentiment analysis. In: Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, Uppsala, pp. 51–59 (2010)Google Scholar
  11. 11.
    Averbuch, M., Karson, T.H., Ben-Ami, B., Maimond, O., Rokachd, L.: Context-sensitive medical information retrieval, In: Proceedings of AMACL, pp. 282–286 (2003)Google Scholar
  12. 12.
    Elberrichi, Z., Rahmoun, A., Bentaalah, M.A.: Using WordNet for text categorization. Int. Arab. J. Inf. Technol. 5(1), 16–24 (2008)Google Scholar
  13. 13.
    Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the 14. Association for Computational Linguistics (ACL), Philadelphia, pp. 417–424 (2002)Google Scholar
  14. 14.
    Rokach, L., Romano, R., Maimon, O.: Negation recognition in medical narrative reports. Inf. Retrieval 11(6), 499–538 (2008)CrossRefGoogle Scholar
  15. 15.
    Fischetti, L., Mon, D., Ritter, J., Rowlands, D.: Electronic Health Record – system functional model, Chapter Three: direct care functions (2007)Google Scholar
  16. 16.
    Stahl, S.A., Shiel, T.G.: Teaching meaning vocabulary: productive approaches for poor readers. Read. Writ. Q. Overcoming Learn. Difficulties 8(2), 223–241 (1992)CrossRefGoogle Scholar
  17. 17.
    Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-03) (2003)Google Scholar
  18. 18.
    Hayuran, H., Sari, S., Adriani, M.: Query and document translation for english-Indonesian cross language IR. In: Peters, C., et al. (eds.) Evaluation of Multilingual and Multi-modal Information Retrieval. Lecture Notes in Computer Science, vol. 4730, pp. 57–61. Springer-Verlag, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Frinculescu, I.C.: An overview of the english influence on the Romanian medical language. Sci. Bull. Politehnica Univ. Timişoara Trans. Mod. Lang. 8, 1–2 (2009)Google Scholar
  20. 20.
    Barbantan, I., Potolea, R.: Towards knowledge extraction from electronic health records - automatic negation identification. In: International Conference on Advancements of Medicine and Health Care through Techonology. Cluj-Napoca, Romania (2014)Google Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  1. 1.Computer Science DepartamentTechnical University of Cluj-NapocaCluj-NapocaRomania

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