Attention all NRDM Users: Effective July 1, 2018 website security has been updated with new password requirements. See details in direct email, or contact Lucy Cafeo at This e-mail address is being protected from spam bots, you need JavaScript enabled to view it with any questions.
Reference Details
Tsui, F., Wagner, M., Cooper, G., Que, J., Harkema, H., Dowling, J., Sriburadej, T., Li, Q., Espino, J. U. and Voorhees, R. (2011), "Probabilistic case detection for disease surveillance using data in electronic medical records", Online J Public Health Inform, 3, 3.

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.
© 2019 Real-time Outbreak and Disease Surveillance Laboratory
Joomla! is Free Software released under the GNU/GPL License.