Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department Without English Translation: A Feasibility Study

Halasz, Sylvia; Brown, Philip; OKTAY, CEM; Cevik, Arif; Kilicaslan, Isa; Goodall, Colin; Cochrane, Dennis; Fowler, Thomas; Jacobson, Guy; Tse, Simon; Allegra, John

doi:10.4137/bii.s11334

Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department Without English Translation: A Feasibility Study

Halasz S., Brown P., OKTAY C., Cevik A. A., Kilicaslan I., Goodall C., ...More

BIOMEDICAL INFORMATICS INSIGHTS, vol.6, pp.29-33, 2013 (Peer-Reviewed Journal)

Publication Type: Article / Article
Volume: 6
Publication Date: 2013
Doi Number: 10.4137/bii.s11334
Journal Name: BIOMEDICAL INFORMATICS INSIGHTS
Page Numbers: pp.29-33
Open Archive Collection: AVESIS Open Access Collection
Eskisehir Osmangazi University Affiliated: Yes

Abstract

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.