title: Classifying free-text triage chief complaints into syndromic categories with natural language processing.
abstract: OBJECTIVE
  Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance.
  
  
  BACKGROUND
  Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form.
  
  
  METHODS
  We implemented a biosurveillance detection system from Pennsylvania to monitor the 2002 Winter Olympic Games. Because input data was in free-text format, we used a natural language processing text classifier to automatically classify free-text triage chief complaints into syndromic categories used by the biosurveillance system. The classifier was trained on 4700 chief complaints from Pennsylvania. We evaluated the ability of the classifier to classify free-text chief complaints into syndromic categories with a test set of 800 chief complaints from Utah.
  
  
  RESULTS
  The classifier produced the following areas under the ROC curve: Constitutional = 0.95; Gastrointestinal = 0.97; Hemorrhagic = 0.99; Neurological = 0.96; Rash = 1.0; Respiratory = 0.99; Other = 0.96. Using information stored in the system's semantic model, we extracted from the Respiratory classifications lower respiratory complaints and lower respiratory complaints with fever with a precision of 0.97 and 0.96, respectively.
  
  
  CONCLUSIONS
  Results suggest that a trainable natural language processing text classifier can accurately extract data from free-text chief complaints for biosurveillance.
authors.affiliation: The RODS Laboratory, Center for Biomedical Informatics, University of Pittsburgh, Suite 8084, Forbes Tower, Pittsburgh, PA 15213, USA. chapman@cbmi.pitt.edu
authors.forename: Wendy W
authors.initials: WW
authors.lastname: Chapman
authors.forename: Lee M
authors.initials: LM
authors.lastname: Christensen
authors.forename: Michael M
authors.initials: MM
authors.lastname: Wagner
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Haug
authors.forename: Oleg
authors.initials: O
authors.lastname: Ivanov
authors.forename: John N
authors.initials: JN
authors.lastname: Dowling
authors.forename: Robert T
authors.initials: RT
authors.lastname: Olszewski
mesh_terms: D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D014218:Triage / Q000379:methods*