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

Authors

  • Fuchiang Tsui Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • Michael Wagner Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • Gregory Cooper Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • Jialan Que Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • Hendrik Harkema Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • John Dowling Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • Thomsun Sriburadej Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • Qi Li Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • Jeremy Espino Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh
  • Ronald Voorhees Garduate School of Public Health, Univ. of Pittsburgh

DOI:

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.

Author Biographies

Fuchiang Tsui, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

PhD

Michael Wagner, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

MD, PhD

Gregory Cooper, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

MD, PhD

Jialan Que, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

MS

Hendrik Harkema, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

PhD

John Dowling, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

MD, MS

Thomsun Sriburadej, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

MS

Qi Li, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

MS

Jeremy Espino, Center for Advanced Study of Informatics in Public Health, Dept. of Biomedical Informatics, Univ. of Pittsburgh

MD

Ronald Voorhees, Garduate School of Public Health, Univ. of Pittsburgh

MD

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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

Issue

Section

Original Articles