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MaNER: A MedicAl Named Entity Recogniser

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Natural Language Processing and Information Systems (NLDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9103))

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Abstract

This paper describes a medicinal products and active ingredients named entity recogniser (MaNER) for Spanish technical documents. This rule-based system uses high quality and low-maintenance lexicons. Our results (F-measure 90 %) proves that dictionary-based approaches, without any deep natural language processing (e.g. POS tagging), can achieve a high performance in this task. Our system obtains better results when compared to similar systems.

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References

  1. AEMPS: NOMENCLATOR DE PRESCRIPCION. http://www.aemps.gob.es/cima/pestanias.do?metodo=nomenclator

  2. Cimino, J.J.: Desiderata for controlled medical vocabularies in the twenty-first century. Meth. Inf. Med. - Author manuscript; available in PMC 2012 August 10 37(4—-5), 394–403 (1998)

    Google Scholar 

  3. Cruanes Vilas, J.: Una aproximación léxico-semántica para el mapeado automático de medicamentos y su aplicación al enriquecimiento de ontologías farmacoterapéuticas. Ph.D. thesis, Universida de Alicante (2014). http://hdl.handle.net/10045/42146

  4. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V., Aswani, N., Roberts, I., Gorrell, G., Funk, A., Roberts, A., Damljanovic, D., Heitz, T., Greenwood, M., Saggion, H., Petrak, J., Li, Y., Peters, W., Al, E.: Developing Language Processing Components with GATE Version 7 (A User Guide), vol. 8 (2012). http://gate.ac.uk

  5. Deléger, L., Grouin, C., Zweigenbaum, P.: Extracting medical information from narrative patient records: the case of medication-related information. J. Am. Med. Inf. Assoc.: JAMIA 17(5), 555–558 (2010). doi:10.1136/jamia.2010.003962

    Article  Google Scholar 

  6. Doan, S., Bastarache, L., Klimkowski, S., Denny, J.C., Xu, H.: Integrating existing natural language processing tools for medication extraction from discharge summaries. J. Am. Med. Inf. Assoc. 17(5), 528–531 (2010). doi:10.1136/jamia.2010.003855

    Article  Google Scholar 

  7. Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, 2009th edn. Cambridge University Press, New York (2009). doi:10.1017/CBO9780511546914

    Google Scholar 

  8. Friedman, C., Rindflesch, T.C., Corn, M.: Natural language processing: state of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine. J. Biomed. Inf. 46(5), 765–773 (2013). doi:10.1016/j.jbi.2013.06.004

    Article  Google Scholar 

  9. González-González, A.I., Sánchez Mateos, J., Sanz Cuesta, T., Riesgo Fuertes, R., Escortell Mayor, E., Hernández Fernández, T.: Estudio de las necesidadesde información generadas por los médicos de atención primaria (proyecto ENIGMA)*. Atención primaria 38(4), 219–224 (2006). http://www.sciencedirect.com/science/article/pii/S0212656706704814

    Article  Google Scholar 

  10. Hamon, T., Grabar, N.: Linguistic approach for identification of medication names and related information in clinical narratives. J. Am. Med. Inf. Assoc. 17(5), 549–554 (2010). doi:10.1136/jamia.2010.004036

    Article  Google Scholar 

  11. Meystre, S.M., Thibault, J., Shen, S., Hurdle, J.F., South, B.R.: Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents. J. Am. Med. Inf. Assoc. 17(5), 559–562 (2010). doi:10.1136/jamia.2010.004028

    Article  Google Scholar 

  12. Moreno, I., Moreda, P., Romá-Ferri, M.: Reconocimiento de entidades nombradas en dominios restringidos. In: Actas del III Workshop en Tecnologías de la Informática, pp. 41–57. Alicante, Spain (2012)

    Google Scholar 

  13. Sanchez-Cisneros, D., Aparicio Gali, F.: UEM-UC3M: an ontology-based named entity recognition system for biomedical texts. In: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), vol. 2, pp. 622–627 (2013). http://www.aclweb.org/anthology/S13-2104

  14. Segura-Bedmar, I., Martnez, P., Herrero-Zazo, M.: SemEval-2013 task 9: extraction of drug-drug interactions from biomedical texts (DDIExtraction 2013). In: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), vol. 2011 (2013). www.aclweb.org/anthology/S13-2056

  15. Segura-Bedmar, I., Revert, R., Martínez, P.: Detecting drugs and adverse events from spanish health social media streams. In: Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi) @ EACL 2014, pp. 106–115 (2014). https://www.aclweb.org/anthology/W/W14/W14-1117.pdf

  16. Tikk, D., Solt, I.: Improving textual medication extraction using combined conditional random fields and rule-based systems. J. Am. Med. Inf. Assoc. 17(5), 540–544 (2010). doi:10.1136/jamia.2010.004119

    Article  Google Scholar 

  17. Uzuner, O., Solti, I., Cadag, E.: Extracting medication information from clinical text. J. Am. Med. Inf. Assoc. 17(5), 514–518 (2010). doi:10.1136/jamia.2010.003947

    Article  Google Scholar 

  18. WHO collaborating center for drug statistics methodology: guidelines for ATC classification and DDD assignment (2015). http://www.whocc.no/atc_ddd_publications/guidelines/

  19. Yang, H.: Automatic extraction of medication information from medical discharge summaries. J. Am. Med. Inf. Assoc. 17(5), 545–548 (2010). doi:10.1136/jamia.2010.003863

    Article  Google Scholar 

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Acknowledgments

This paper has been partially supported by the Spanish Government (grant no. TIN2012-38536-C03-03 and TIN2012-31224)

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Correspondence to Isabel Moreno .

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Moreno, I., Moreda, P., Romá-Ferri, M.T. (2015). MaNER: A MedicAl Named Entity Recogniser. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_40

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  • DOI: https://doi.org/10.1007/978-3-319-19581-0_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19580-3

  • Online ISBN: 978-3-319-19581-0

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