Probabilistic case detection for disease surveillance using data in electronic medical records
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Published on: May 15, 2020
Fuchiang Tsui1, Michael Wagner, Gregory Cooper
1Center for Advanced Study of Informatics in Public Health, Department of Biomedical Informatics, 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.
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