title: Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department Without English Translation: A Feasibility Study.
abstract: BACKGROUND
  Syndromic surveillance is designed for early detection of disease outbreaks. An important data source for syndromic surveillance is free-text chief complaints (CCs), which are generally recorded in the local language. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories. The n-gram classifier is created by using text fragments to measure associations between chief complaints (CC) and a syndromic grouping of ICD codes.
  
  
  OBJECTIVE
  The objective was to create a Turkish n-gram CC classifier for the respiratory syndrome and then compare daily volumes between the n-gram CC classifier and a respiratory ICD-10 code grouping on a test set of data.
  
  
  METHODS
  The design was a feasibility study based on retrospective cohort data. The setting was a university hospital emergency department (ED) in Turkey. Included were all ED visits in the 2002 database of this hospital. Two of the authors created a respiratory grouping of International Classification of Diseases, 10th Revision ICD-10-CM codes by consensus, chosen to be similar to a standard respiratory (RESP) grouping of ICD codes created by the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE), a project of the Centers for Disease Control and Prevention. An n-gram method adapted from AT&T Labs' technologies was applied to the first 10 months of data as a training set to create a Turkish CC RESP classifier. The classifier was then tested on the subsequent 2 months of visits to generate a time series graph and determine the correlation with daily volumes measured by the CC classifier versus the RESP ICD-10 grouping.
  
  
  RESULTS
  The Turkish ED database contained 30,157 visits. The correlation (R (2)) of n-gram versus ICD-10 for the test set was 0.78.
  
  
  CONCLUSIONS
  The n-gram method automatically created a CC RESP classifier of the Turkish CCs that performed similarly to the ICD-10 RESP grouping. The n-gram technique has the advantage of systematic, consistent, and rapid deployment as well as language independence.
authors.affiliation: AT&T Interactive, Glendale, CA, USA.
authors.forename: Sylvia
authors.initials: S
authors.lastname: Halász
authors.forename: Philip
authors.initials: P
authors.lastname: Brown
authors.forename: Cem
authors.initials: C
authors.lastname: Oktay
authors.forename: Arif Alper
authors.initials: AA
authors.lastname: Cevik
authors.forename: Isa
authors.initials: I
authors.lastname: Kılıçaslan
authors.forename: Colin
authors.initials: C
authors.lastname: Goodall
authors.forename: Dennis G
authors.initials: DG
authors.lastname: Cochrane
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Fowler
authors.forename: Guy
authors.initials: G
authors.lastname: Jacobson
authors.forename: Simon
authors.initials: S
authors.lastname: Tse
authors.forename: John R
authors.initials: JR
authors.lastname: Allegra
keywords: disease outbreaks; epidemiology; n-gram; public health; surveillance