Caracterización de un corpus extraído de historias clínicas electrónicas de maternas a través de técnicas de procesamiento de lenguaje natural

María Camila Durango Barrera, Ever Augusto Torres Silva, José Fernando Florez-Arango, Andrés Orozgo-Duque

Producción científica: Contribución a una revistaArtículo en revista científica indexadarevisión exhaustiva


The purpose of this article was to characterize the free text available in an electronic health record of an institution, directed at the care of patients in pregnancy. More than being a data repository, the electronic health record (HCE) has become a clinical decision support system (CDSS). However, due to the high volume of information, as some of the key information in EHR is in free text form, using the full potential that EHR information offers to improve clinical decision-making requires the support of methods of text mining and natural language processing (PLN). Particularly in the area of gynecology and obstetrics, the implementation of PLN methods could help speed up the identification of factors associated with maternal risk. Despite this, in the literature there are no papers that integrate PLN techniques in EHR associated with maternal follow-up in Spanish. Taking into account this knowledge gap, in this work a corpus was generated and characterized from the EHRs of a gynecology and obstetrics service characterized by treating high-risk maternal patients. PLN and text mining methods were implemented on the data, obtaining 659 789 tokens and a dictionary with unique words given by 7 334 tokens. The characterization of the data was developed from the identification of the most frequent words and n-grams and a vector representation of embedding words in a 300-dimensional space was performed using a CBOW (Continuous Bag Of Words) neural network architecture. The embedding of words allowed to verify by means of Clustering algorithms, that the words associated to the same group can come to represent associations referring to types of patients, or group similar words, including words written with spelling errors. The corpus generated and the results found lay the foundations for future work in the detection of entities (symptoms, signs, diagnoses, treatments), correction of spelling errors and semantic relationships between words to generate summaries of medical records or assist the follow-up of mothers through the automated review of the electronic health record.

Título traducido de la contribuciónCharacterization of a corpus extracted from maternal electronic health records through natural language processing techniques
Idioma originalEspañol
Número de artículoe1776
PublicaciónRevista Cubana de Informacion en Ciencias de la Salud
EstadoPublicada - 1 oct. 2021
Publicado de forma externa

Nota bibliográfica

Publisher Copyright:
© 2021, Centro Nacional de Informacion de Ciencias Medicas. All rights reserved.

Palabras clave

  • Natural language processing
  • artificial neural networks
  • electronic health record
  • machine learning
  • word embedding


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