Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
PDF
HTML

How to Cite

Tsui, F., Wagner, M., Cooper, G., Que, J., Harkema, H., Dowling, J., Sriburadej, T., Li, Q., Espino, J., & Voorhees, R. (2011). Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records. Online Journal of Public Health Informatics, 3(3). https://doi.org/10.5210/ojphi.v3i3.3793

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.
https://doi.org/10.5210/ojphi.v3i3.3793
PDF
HTML
Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. Share-alike: when posting copies or adaptations of the work, release the work under the same license as the original. For any other use of articles, please contact the copyright owner. The journal/publisher is not responsible for subsequent uses of the work, including uses infringing the above license. It is the author's responsibility to bring an infringement action if so desired by the author.