Talking Turkish: Using N-Grams for Syndromic Surveillance in a Turkish Emergency Department without the Need for English Translation


Previously we used an “N-Gram” classifier for syndromic surveillance of emergency department (ED) chief complaints (CC) in English for bioterrorism. The classifier is trained on a set of ED visits for which both the ICD diagnosis code and CC are available by measuring the associations of text fragments within the CC (e.g. 3 characters for a “3-gram”) with a syndromic group of ICD codes. Because the ICD system is language independent, the technique has the potential advantage of rapid automated deployment in multiple languages. Our objective was to apply the N-Gram method to a training set of Turkish ED data to create a Turkish CC classifier for the respiratory syndrome (RESP) and determine its performance in a test set.



To determine how closely the performance of an ngram CC classifier for the RESP syndrome matched the performance of the ICD9 classifier.

Original Publication Year: 
Event/Publication Date: 
October, 2006

July 30, 2018

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