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

Shared Electronic Health Records and Health Information Systems (68) 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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Article Authors Cited by (12) Tweetations Metrics

Journals

  1. Ferraro J, Ye Y, Gesteland P, Haug P, Tsui F, Cooper G, Bree R, Ginter T, Nowalk A, Wagner M. The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance. Applied Clinical Informatics 2017;08(02):560 View
  2. López Pineda A, Ye Y, Visweswaran S, Cooper G, Wagner M, Tsui F. Comparison of machine learning classifiers for influenza detection from emergency department free-text reports. Journal of Biomedical Informatics 2015;58:60 View
  3. Hung S, Wu C, Singh A, Li J, Lee C, Chou E, Pekosz A, Rothman R, Chen K. Developing and validating clinical features-based machine learning algorithms to predict influenza infection in influenza-like illness patients. Biomedical Journal 2023;46(5):100561 View
  4. Aronis J, Millett N, Wagner M, Tsui F, Ye Y, Ferraro J, Haug P, Gesteland P, Cooper G. A Bayesian system to detect and characterize overlapping outbreaks. Journal of Biomedical Informatics 2017;73:171 View
  5. Ford E, Carroll J, Smith H, Scott D, Cassell J. Extracting information from the text of electronic medical records to improve case detection: a systematic review. Journal of the American Medical Informatics Association 2016;23(5):1007 View
  6. Kulik T, Brankovics B, van Diepeningen A, Bilska K, Żelechowski M, Myszczyński K, Molcan T, Stakheev A, Stenglein S, Beyer M, Pasquali M, Sawicki J, Wyrȩbek J, Baturo-Cieśniewska A. Diversity of Mobile Genetic Elements in the Mitogenomes of Closely Related Fusarium culmorum and F. graminearum sensu stricto Strains and Its Implication for Diagnostic Purposes. Frontiers in Microbiology 2020;11 View
  7. Ye Y, Wagner M, Cooper G, Ferraro J, Su H, Gesteland P, Haug P, Millett N, Aronis J, Nowalk A, Ruiz V, López Pineda A, Shi L, Van Bree R, Ginter T, Tsui F, Chuang J. A study of the transferability of influenza case detection systems between two large healthcare systems. PLOS ONE 2017;12(4):e0174970 View
  8. Tsui F, Ye Y, Ruiz V, Cooper G, Wagner M. Automated influenza case detection for public health surveillance and clinical diagnosis using dynamic influenza prevalence method. Journal of Public Health 2018;40(4):878 View
  9. Aronis J, Ye Y, Espino J, Hochheiser H, Michaels M, Cooper G. A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases: Algorithm Development and Validation. JMIR Public Health and Surveillance 2024;10:e57349 View
  10. Yokokawa D, Uehara T, Ohira Y, Noda K, Higuchi N, Kikuchi E, Enatsu K, Ikusaka M. A Cross-Sectional Study on Whether Comprehensively Gathering Information From Medical Records Is Useful for the Collection of Operational Characteristics. Cureus 2024 View