This paper is in the following e-collection/theme issue:

Presentations, Abstracts, or Demonstrations (740) Influenza and Influenza-Like-Illness (ILI) Surveillance (203)

Published on in Vol 8, No 1 (2016):

Natural Language Processing and Technical Challenges of Influenza-Like Illness Surveillance

Natural Language Processing and Technical Challenges of Influenza-Like Illness Surveillance

Natural Language Processing and Technical Challenges of Influenza-Like Illness Surveillance

Authors of this article:

Dino P. Rumoro ;   Gillian S. Gibbs ;   Shital C. Shah ;   Marilyn M. Hallock ;   Gordon M. Trenholme ;   Michael J. Waddell ;   Joseph P. Bernstein
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    Processing free-text clinical information in an electronic medical record may enhance surveillance systems for early identification of influenza-like illness outbreaks. However, processing clinical text using natural language processing (NLP) poses a challenge in preserving the semantics of the original information recorded. In this study, we discuss several NLP and technical issues as well as potential solutions for implementation in syndromic surveillance systems.