Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
DOI:
https://doi.org/10.5210/ojphi.v3i3.3793Abstract
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.Published
2011-12-22
How to Cite
Tsui, F., Wagner, M., Cooper, G., Que, J., Harkema, H., Dowling, 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
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Section
Original Articles