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Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
Authors:Fuchiang Tsui  Michael Wagner  Gregory Cooper  Jialan Que  Hendrik Harkema  John Dowling  Thomsun Sriburadej  Qi Li  Jeremy U. Espino  Ronald Voorhees
Affiliation:1.Center for Advanced Study of Informatics in Public Health, Department of Biomedical Informatics, University of Pittsburgh;2.Graduate School of Public Health, University of Pittsburgh
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.
Keywords:case detection   disease surveillance   influenza   electronic medical records
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