Descubrimiento de conocimiento en historias clínicas mediante minería de texto

Ana Isabel Oviedo Carrascal, David Sanguino Cotte, Natalia Andrea Restrepo Arango, Andrés Felipe Patiño Vélez

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)

Resumen

The clinical institutions generate a large amount of unstructured data both in the registration of procedures in free text by medical staff, and by the images and videos generated by diagnostic aids. This paper proposes a process of knowledge discovery in the unstructured text of the medical records of the trauma area of the San Vicente Foundation Hospital through text mining. Text preparation techniques were applied such as elimination of non-relevant words, substitution of terms, elimination of accents and derivation of words. Regarding mining processes, supervised and unsupervised learning techniques were applied such as decision trees, logistic regression, nearest k-neighbors, hierarchical clustering and association rules. The result obtained is the conformation of a model of the most relevant words in the clinical records of the Hospital in the area of traumatology.

Título traducido de la contribuciónKnowledge discovery in medical records through text mining
Idioma originalEspañol
Páginas (desde-hasta)29-43
Número de páginas15
PublicaciónRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
Volumen2019
N.º34
DOI
EstadoPublicada - oct. 2019
Publicado de forma externa

Nota bibliográfica

Publisher Copyright:
© 2019, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Palabras clave

  • Health data mining
  • Natural language processing
  • Text mining

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