Extracción no supervisada de relaciones entre medicamentos y efectos adversos

• Autores: Andrés Duque Fernández , Juan Martínez Romo , Lourdes Araujo
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 55, 2015, págs. 83-90
• Idioma: español
• Títulos paralelos:
  • Unsupervised extraction of adverse drug reaction relationships
• Enlaces
  • Texto completo
• Resumen
  • español

    En este trabajo se presentan los resultados preliminares de una nueva técnica no supervisada para la extracción de relaciones entre medicamentos y efectos adversos. La identificación de relaciones se consigue a partir de un modelo de representación de conocimiento que extrae pares de entidades con un peso determinado, en función de la significatividad estadística de su coaparición en un mismo documento. Dicho modelo puede ser posteriormente convertido en un grafo. El sistema ha sido evaluado sobre un corpus de referencia, denominado ADE corpus, consiguiendo resultados prometedores al obtener una eficacia muy por encima de un baseline estándar. Las primeras pruebas también muestran un alto potencial para inducir conocimiento nuevo.

  • English

    In this work we present preliminary results obtained by a new unsupervised technique for extracting relations between drugs and adverse drug reactions. The identification of those relations is achieved using a knowledge representation model that generates pairs of entities and assigns them a specific weight, depending on the statistical significance of their co-occurrence in the same document. This model may subsequently be transformed into a graph. The system has been evaluated over the reference ADE corpus, obtaining promising results, since its effectiveness is quite higher than that obtained by a standard basline. First tests also show a high potential for inducing new knowledge.

• Referencias bibliográficas
  • Aramaki, E., Y. Miura, M. Tonoike, T. Ohkuma, H. Masuichi, K. Waki, y K. Ohe. 2010. Extraction of adverse drug effects from clinical records....
  • Donelly, K. 2006. SNOMED-CT: The advanced terminology and coding system for eHealth. Studies in health technology and informatics, 121:279–290.
  • Edwards, I. R. y J. K. Aronson. 2000. Adverse drug reactions: definitions, diagnosis, and management. The Lancet, 356(9237):1255 – 1259.
  • Eltyeb, S. y N. Salim. 2015. Pattern-based system to detect the adverse drug effect sentences in medical case reports. Journal of Theoretical...
  • Gurulingappa, H., J. Fluck, M. HofmannApitius, y L. Toldo. 2011. Identification of adverse drug event assertive sentences in medical case...
  • Gurulingappa, H., A. Mateen-Rajput, y L. Toldo. 2012. Extraction of potential adverse drug events from medical case reports. Journal of biomedical...
  • Gurulingappa, H., A. Mateen Rajput, A. Roberts, J. Fluck, M. Hofmann-Apitius, y L. Toldo. 2012. Development of a benchmark corpus to support...
  • Kandula, S. y Q. Zeng-Treitler. 2010. Exploring relations among semantic groups: a comparison of concept co-occurrence in biomedical sources....
  • Kang, N., E.M. van Mulligen, B. Singh, C. Bui, Z. Afzal, y J. Kors. 2014. Knowledge-based extraction of adverse drug events from biomedical...
  • Lindberg, D., B. Humphreys, y A. McCray. 1993. The unified medical language system. Methods of Information in Medicine, 32(4):281–291.
  • Pyysalo, S., A. Airola, J. Heimonen, J. Bjorne, F. Ginter, y T. Salakoski. 2008. Comparative analysis of five protein-protein interaction...
  • Segura-Bedmar, I., S. de la Pena González, y P. Martínez. 2014. Extracting drug indications and adverse drug reactions from spanish health...
  • Wang, X., G. Hripcsak, M. Markatou, y C. Friedman. 2009. Active computerized pharmacovigilance using natural language processing,statistics,...