Ensembles for clinical entity extraction

  1. Rebecka Weegar
  2. Alicia Pérez
  3. Hercules Dalianis
  4. Koldo Gojenola
  5. Arantza Casillas
  6. Maite Oronoz
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2018

Número: 60

Páginas: 13-20

Tipo: Artículo

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Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

Los informes médicos son una valiosa fuente de conocimiento clínico. Las técnicas de Procesamiento del Lenguaje Natural han sido aplicadas al procesamiento de informes médicos para diversas aplicaciones. Generalmente un primer paso es la detección de entidades médicas: identifcar medicamentos, enfermedades y partes del cuerpo. Sin embargo, la mayoría de los trabajos se han desarrollado para informes en Inglés. El objetivo de este trabajo es mejorar el reconocimiento de entidades médicas para otras lenguas diferentes a Inglés, comparando los mismos métodos en dos lenguas y utilizando agrupaciones de modelos. Los modelos han sido creados para informes médicos en Español y Sueco utilizando SVM, Perceptron, CRF y cuatro conjuntos diferentes de atributos, incluyendo atributos no supervisados. Para el modelo combinado se ha aplicado votación ponderada teniendo en cuenta la F-measure individual. En conclusión, el modelo combinado mejora el rendimiento general y para posibles mejoras debemos investigar métodos más sofisticados de agrupación.

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Información de financiación

This work has been partially funded by the Spanish ministry (PROSAMED: TIN2016- 77820-C3-1-R, TADEEP: TIN2015-70214-P), the Basque Government (DETEAMI: 2014111003), the University of the Basque Country UPV-EHU (MOV17/14) and the Nordic Center of Excellence in Health-Related e-Sciences (NIASC).

Financiadores

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Fuente de los datos: Dialnet