Linked Life Data

18693934

Source:http://linkedlifedata.com/resource/pubmed/id/18693934

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-8-12
pubmed:abstractText
Emergency Department (ED) chief complaint (CC) data are key components of syndromic surveillance systems. However, it is difficult to use CC data because they are not standardized and contain varying semantic and lexical forms for the same concept. The purpose of this project was to revise a previously-developed text processor for pre-processing CC data specifically for syndromic surveillance and then evaluate it for acute respiratory illness surveillance to support decisions by public health epidemiologists. We evaluated the text processor accuracy and used the results to customize it for respiratory surveillance. We sampled 3,699 ED records from a population-based public health surveillance system. We found equal sensitivity, specificity, and positive and negative predictive value of syndrome queries of data processed through the text processor compared to a standard keyword method on raw, unprocessed data.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1942-597X
pubmed:author
pubmed:issnType
Electronic
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
736-40
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Evaluation of a chief complaint pre-processor for biosurveillance.
pubmed:affiliation
University of North Carolina, Chapel Hill, NC, USA.
pubmed:publicationType
Journal Article, Evaluation Studies, Research Support, N.I.H., Extramural