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

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 d...

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Autores principales: Tsui, Fuchiang, Wagner, Michael, Cooper, Gregory, Que, Jialan, Harkema, Hendrik, Dowling, John, Sriburadej, Thomsun, Li, Qi, Espino, Jeremy U., Voorhees, Ronald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: University of Illinois at Chicago Library 2011
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3615792/
https://www.ncbi.nlm.nih.gov/pubmed/23569615
http://dx.doi.org/10.5210/ojphi.v3i3.3793
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author Tsui, Fuchiang
Wagner, Michael
Cooper, Gregory
Que, Jialan
Harkema, Hendrik
Dowling, John
Sriburadej, Thomsun
Li, Qi
Espino, Jeremy U.
Voorhees, Ronald
author_facet Tsui, Fuchiang
Wagner, Michael
Cooper, Gregory
Que, Jialan
Harkema, Hendrik
Dowling, John
Sriburadej, Thomsun
Li, Qi
Espino, Jeremy U.
Voorhees, Ronald
author_sort Tsui, Fuchiang
collection PubMed
description 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.
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spelling pubmed-36157922013-04-08 Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records Tsui, Fuchiang Wagner, Michael Cooper, Gregory Que, Jialan Harkema, Hendrik Dowling, John Sriburadej, Thomsun Li, Qi Espino, Jeremy U. Voorhees, Ronald Online J Public Health Inform Articles 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. University of Illinois at Chicago Library 2011-12-22 /pmc/articles/PMC3615792/ /pubmed/23569615 http://dx.doi.org/10.5210/ojphi.v3i3.3793 Text en ©2011 the author(s) http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/ojphi/about/submissions#copyrightNotice This is an Open Access article. 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.
spellingShingle Articles
Tsui, Fuchiang
Wagner, Michael
Cooper, Gregory
Que, Jialan
Harkema, Hendrik
Dowling, John
Sriburadej, Thomsun
Li, Qi
Espino, Jeremy U.
Voorhees, Ronald
Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
title Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
title_full Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
title_fullStr Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
title_full_unstemmed Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
title_short Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records
title_sort probabilistic case detection for disease surveillance using data in electronic medical records
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3615792/
https://www.ncbi.nlm.nih.gov/pubmed/23569615
http://dx.doi.org/10.5210/ojphi.v3i3.3793
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