RESUMEN
INTRODUCTION: 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. OBJECTIVES: 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. CONCLUSION: 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.
RESUMEN
INTRODUCTION: 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. OBJECTIVES: For gastrointestinal (GI) syndrome to determine: (1) ngram CC classifier sensitivity/specificity. (2) Daily volumes for ngram CC and ICD-9-CM classifiers. DESIGN: Retrospective cohort. SETTING: 19 Emergency Departments. PARTICIPANTS: Consecutive visits (1/1/2000-12/31/2005). PROTOCOL: (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. CONCLUSION: 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.