title: The ngram chief complaint classifier: a novel method of automatically creating chief complaint classifiers based on international classification of diseases groupings.
abstract: BACKGROUND
  The ngram classifier is created by using text fragments to measure associations between chief complaints (CC) and a syndromic grouping of ICD-9-CM codes.
  
  
  OBJECTIVE
  For gastrointestinal (GI) syndrome to determine: (1) ngram CC classifier sensitivity/specificity. (2) Daily volumes for ngram CC and ICD-9-CM classifiers.
  
  
  METHODS
  
  
  
  METHODS
  Retrospective cohort.
  
  
  METHODS
  19 Emergency Departments.
  
  
  METHODS
  Consecutive visits (1/1/2000-12/31/2005).
  
  
  METHODS
  (1) Used an existing ICD-9-CM filter for "lower GI" to create the ngram CC classifier from a training set and then measured sensitivity/specificity in a test set using an ICD-9-CM classifier as criterion. (2) Compare daily volumes based on ICD-9-CM with that predicted by the ngram classifier.
  
  
  RESULTS
  For a specificity of 0.96, sensitivity was 0.70. The daily volume correlation for ngram vs. ICD-9-CM was R=0.92.
  
  
  CONCLUSIONS
  The ngram CC classifier performed similarly to manually developed CC classifiers and has advantages of rapid automated creation and updating, and may be used independent of language or dialect.
authors.affiliation: AT&T Labs-Research, Florham Park, NJ, USA.
authors.forename: Philip
authors.initials: P
authors.lastname: Brown
authors.forename: Sylvia
authors.initials: S
authors.lastname: Halász
authors.forename: Colin
authors.initials: C
authors.lastname: Goodall
authors.forename: Dennis G
authors.initials: DG
authors.lastname: Cochrane
authors.forename: Peter
authors.initials: P
authors.lastname: Milano
authors.forename: John R
authors.initials: JR
authors.lastname: Allegra
mesh_terms: D015331:Cohort Studies; D003933:Diagnosis; D004196:Disease Outbreaks / Q000517:prevention & control / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital; D004812:Epidemiologic Methods; D005767:Gastrointestinal Diseases; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D009323:Natural Language Processing; D011159:Population Surveillance / Q000379:methods*; D012189:Retrospective Studies; D012680:Sensitivity and Specificity