@techreport{TD:100688,
	att_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.}},
	att_authors={sh3258, pb2735, cg2198, gj2418, st2196},
	att_categories={C_CCF.1, C_CCF.9, C_IIS.2, A_ST.4},
	att_copyright={{}},
	att_copyright_notice={{The definitive version was published in   2012  . {{, Issue 2013:6}}{{, 2013-04-25}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={disease outbreaks,  epidemiology,  public health,  surveillance,  n-gram,  data mining,  algorithm,  statistics,  text mining,  classifier,  classification algorithm},
	att_techdoc={true},
	att_techdoc_key={TD:100688},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100688_DS1_2013-04-26T18:46:50.038Z.pdf},
	author={Sylvia Halasz and Philip Brown and Cem Oktay and Arif Alper Cevik and Isa Kilicaslan and Colin Goodall and Dennis Cochrane and Tom Fowler and Guy Jacobson and Simon Tse and John Allegra},
	institution={{Biomedical Informatics Insights}},
	month={April},
	title={{Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department without English Translation: A Feasibility Study}},
	year=2013,
}