The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

  1. Alicia Pérez
  2. Arantza Casillas
  3. Koldo Gojenola
  4. Maite Oronoz
  5. Nerea Aguirre
  6. Estibaliz Amillano
Aldizkaria:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Argitalpen urtea: 2014

Zenbakia: 53

Orrialdeak: 77-84

Mota: Artikulua

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Beste argitalpen batzuk: Procesamiento del lenguaje natural

Laburpena

La red de hospitales que configuran el sistema español de sanidad utiliza la Clasificación Internacional de Enfermedades Modificación Clínica (ICD9-CM) para codificar partes de alta hospitalaria. Hoy en día, este trabajo lo realizan a mano los expertos. Este artículo aborda la problemática de clasificar automáticamente partes reales de alta hospitalaria escritos en español teniendo en cuenta el estándar ICD9-CM. El desafío radica en que los partes hospitalarios están escritos con lenguaje espontáneo. Hemos experimentado con varios sistemas de aprendizaje automático para solventar este problema de clasificación. El algoritmo Random Forest es el más competitivo de los probados, obtiene un F-measure de 0.876.

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