Ensembles for clinical entity extraction

• Autores: Rebecka Weegar, Alicia Pérez Ramírez , Hercules Dalianis, Koldobika Gojenola Galletebeitia , Arantza Casillas Rubio , Maite Oronoz Anchordoqui
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 60, 2018, págs. 13-20
• Idioma: inglés
• Títulos paralelos:
  • Agrupaciones para la extracción de entidades clínicas
• Enlaces
  • Texto completo
• Resumen
  • español

    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.

  • English

    Health records are a valuable source of clinical knowledge and Natural Language Processing techniques have previously been applied to the text in health records for a number of applications. Often, a first step in clinical text processing is clinical entity recognition; identifying, for example, drugs, disorders, and body parts in clinical text. However, most of this work has focused on records in English.

    Therefore, this work aims to improve clinical entity recognition for languages other than English by comparing the same methods on two diferent languages, specifically by employing ensemble methods. Models were created for Spanish and Swedish health records using SVM, Perceptron, and CRF and four diferent feature sets, including unsupervised features. Finally, the models were combined in ensembles.

    Weighted voting was applied according to the models individual F-scores. In conclusion, the ensembles improved the overall performance for Spanish and the precision for Swedish.

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