• DocumentCode
    677853
  • Title

    Challenges in Clinical Named Entity Recognition for Decision Support

  • Author

    Dehghan, Afshin ; Keane, John A. ; Nenadic, Goran

  • Author_Institution
    Sch. of Comput. Sci., Univ. of Manchester, Manchester, UK
  • fYear
    2013
  • fDate
    13-16 Oct. 2013
  • Firstpage
    947
  • Lastpage
    951
  • Abstract
    In addition to structured data, electronic health records contain unstructured clinical notes and narratives. The identification and classification of mentions of relevant clinical concepts is a crucial preprocessing step in designing and developing clinical decision support systems. While this task has gained significant attention in recent years, there are still a number of issues that need further investigation. This paper explores a variety of common challenges faced by clinical named entity recognition and classification methods as well as current approaches to handling them.
  • Keywords
    data structures; decision support systems; electronic health records; pattern classification; classification methods; clinical decision support systems; clinical named entity recognition; electronic health records; structured data; unstructured clinical narratives; unstructured clinical notes; Availability; Data mining; Diseases; Drugs; Medical diagnostic imaging; Terminology; Unified modeling language; Clinical concept extraction; Clinical named entity recognition and classification; Information extraction; Text mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
  • Conference_Location
    Manchester
  • Type

    conf

  • DOI
    10.1109/SMC.2013.166
  • Filename
    6721919