This paper is in the following e-collection/theme issue:

Shared Electronic Health Records and Health Information Systems (72) Theme Issue: Bayesian Methods In Medical Research (26)

Published on in Vol 3, No 3 (2011):

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

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

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

Authors of this article:

Fuchiang Tsui1 ;   Michael Wagner1 ;   Gregory Cooper1 ;   Jialan Que1 ;   Hendrik Harkema1 ;   John Dowling1 ;   Thomsun Sriburadej1 ;   Qi Li1 ;   Jeremy Espino1 ;   Ronald Voorhees2
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    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.