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.