title: Probabilistic case detection for disease surveillance using data in electronic medical records.
abstract: This paper describes a probabilistic case detection system (CDS) that uses a Bayesian network model of medical diagnosis and natural language processing to compute the posterior probability of influenza and influenza-like illness from emergency department dictated notes and laboratory results. The diagnostic accuracy of CDS for these conditions, as measured by the area under the ROC curve, was 0.97, and the overall accuracy for NLP employed in CDS was 0.91.
authors.affiliation: Center for Advanced Study of Informatics in Public Health, Department of Biomedical Informatics, University of Pittsburgh.
authors.forename: Fuchiang
authors.initials: F
authors.lastname: Tsui
authors.forename: Michael
authors.initials: M
authors.lastname: Wagner
authors.forename: Gregory
authors.initials: G
authors.lastname: Cooper
authors.forename: Jialan
authors.initials: J
authors.lastname: Que
authors.forename: Hendrik
authors.initials: H
authors.lastname: Harkema
authors.forename: John
authors.initials: J
authors.lastname: Dowling
authors.forename: Thomsun
authors.initials: T
authors.lastname: Sriburadej
authors.forename: Qi
authors.initials: Q
authors.lastname: Li
authors.forename: Jeremy U
authors.initials: JU
authors.lastname: Espino
authors.forename: Ronald
authors.initials: R
authors.lastname: Voorhees
keywords: case detection; disease surveillance; electronic medical records; influenza