Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP), the recognition of negation modifiers represents an important challenge. A wide range of cNLP applications have been developed to detect the negation of medical entities in clinical free-text, however, effective solutions for languages other than English are scarce. This study aimed at developing a solution for negation recognition in Spanish EHRs based on a combination of a customized rule-based NegEx layer and a convolutional neural network (CNN).

Authors

  • Guillermo Argüello-González
    MedSavana SL, Madrid, 28004, Spain.
  • José Aquino-Esperanza
    MedSavana SL, Madrid, 28004, Spain.
  • Daniel Salvador
    MedSavana SL, Madrid, 28004, Spain.
  • Rosa Bretón-Romero
    Savana Research, Madrid, SL, 28004, Spain.
  • Carlos Del Río-Bermudez
    Savana Research, Madrid, SL, 28004, Spain.
  • Jorge Tello
    MedSavana SL, Madrid, 28004, Spain.
  • Sebastian Menke
    MedSavana SL, Madrid, 28004, Spain. smenke@savanamed.com.