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


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

BIOMEDICAL INFORMATICS INSIGHTS, cilt.6, ss.29-33, 2013 (ESCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 6
  • Basım Tarihi: 2013
  • Doi Numarası: 10.4137/bii.s11334
  • Dergi Adı: BIOMEDICAL INFORMATICS INSIGHTS
  • Sayfa Sayıları: ss.29-33

Özet

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.