Medical Entity Recognition and Negation Extraction: Assessment of NegEx on Health Records in Spanish

  • Sara Santiso
  • Arantza Casillas
  • Alicia Pérez
  • Maite Oronoz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10208)

Abstract

This work focuses on biomedical text mining. The core of this work is to make a step ahead in the negation detection of biomedical entities on Electronic Health Records (EHRs), where the detection of non-negated entities is as important as the identification of negated entities. For instance, the identification of a negated entity as factual, can produce diagnostic errors in decision support systems.

Negated entity recognition tackles two tasks: (1) entity recognition; (2) entity classification as negated or not. To identify negations, in the literature rule-based and machine-learning techniques have been used. This paper presents an adaptation of the rule-based system NegEx, which uses exact-matching for the aforementioned tasks.

Our contribution consist in assessing the aforementioned two tasks and explored alternatives for each of them, in such a way that the negation detection improves when the entity recognition is able to detect more entities correctly.

The evaluation was carried out within a real domain of 75 EHRs written in Spanish obtaining an f-measure of 76.2 for entity recognition and 73.8 for negation detection.

Keywords

Negation detection Electronic health records Text mining Spanish 

References

  1. 1.
    Blanco, E., Moldovan, D.I.: Some issues on detecting negation from text. In: FLAIRS (2011)Google Scholar
  2. 2.
    Bretonnel, K., Demmer-Fushman, D.: Biomedical Natural Language Processing, vol. 11. John Benjamins Publishing Company, Amsterdam (2014)CrossRefGoogle Scholar
  3. 3.
    Ceusters, W., Elkin, P., Smith, B.: Negative findings in electronic health records and biomedical ontologies: a realist approach. Int. J. Med. Inform. 76, 326–333 (2017)Google Scholar
  4. 4.
    Chapman, W.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
  5. 5.
    Costumero, R., Lopez, F., Gonzalo-Martín, C., Millan, M., Menasalvas, E.: An approach to detect negation on medical documents in Spanish. In: Ślȩzak, D., Tan, A.-H., Peters, J.F., Schwabe, L. (eds.) BIH 2014. LNCS (LNAI), vol. 8609, pp. 366–375. Springer, Heidelberg (2014). doi:10.1007/978-3-319-09891-3_34 Google Scholar
  6. 6.
    Henriksson, A., Kvist, M., Dalianis, H., Duneld, M.: Identifying adverse drug event information in clinical notes with distributional semantic representations of context. J. Biomed. Inform. 57, 333–349 (2015)CrossRefGoogle Scholar
  7. 7.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML, vol. 1, pp. 282–289 (2001)Google Scholar
  8. 8.
    Nakov, P., Zesch, T. (eds.): Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). Association for Computational Linguistics and Dublin City University, Dublin, Ireland (2014)Google Scholar
  9. 9.
    Nawaz, R., Thompson, P., Ananiadou, S.: Negated bio-events: analysis and identification. BMC Bioinform. 14, 14 (2013)CrossRefGoogle Scholar
  10. 10.
    Oronoz, M., Gojenola, K., Pérez, A., de Ilarraza, A.D., Casillas, A.: On the creation of a clinical gold standard corpus in Spanish: mining adverse drug reactions. J. Biomed. Inform. 56, 318–332 (2015)CrossRefGoogle Scholar
  11. 11.
    Skeppstedt, M.: Negation detection in swedish clinical text. In: Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents, pp. 15–21. Association for Computational Linguistics (2010)Google Scholar
  12. 12.
    Skeppstedt, M., Dalianis, H., Nilsson, G.H.: Retrieving disorders and findings: results using SNOMED CT and NegEx adapted for swedish. In: Third International Workshop on Health Document Text Mining and Information AnalysisBled, Slovenia, 6 July 2011, Bled Slovenia, Collocated with AIME 2011, pp. 11–17 (2011)Google Scholar
  13. 13.
    Weegar, R., Kvist, M., Sundström, K., Brunak, S., Dalianis, H.: Finding cervical cancer symptoms in swedish clinical text using a machine learning approach and NegEx. In: AMIA Annual Symposium Proceedings. vol. 2015, p. 1296. American Medical Informatics Association (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sara Santiso
    • 1
  • Arantza Casillas
    • 1
  • Alicia Pérez
    • 1
  • Maite Oronoz
    • 1
  1. 1.IXA GroupUniversity of the Basque Country (UPV-EHU)DonostiaSpain

Personalised recommendations