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RODS

  • Hsinchun Chen
  • Daniel Zeng
  • Ping Yan
Part of the Integrated Series in Information Systems book series (ISIS, volume 21)

Abstract

The Real-time Outbreak and Disease Surveillance (RODS) system was initiated by the RODS Laboratory at the University of Pittsburgh in 1999. The system is now an open source project under the GNU license. The RODS development effort has been organized into seven functional areas: overall design, data collection, syndrome classification, database and data warehousing, outbreak detection algorithms, data access, and user interfaces. Each functional area has a coordinator for the open source project, and there is an overall coordinator responsible for the architecture, overall integration of components, and overall quality of the JAVA source code. Figure 8-1 illustrates the RODS' system architecture.

The RODS system as a syndromic surveillance application was originally deployed in Pennsylvania, Utah, and Ohio. As of 2006, RODS performs emergency department surveillance for other states of California, Illinois, Kentucky, Michigan, New Jersey, Nevada, and Wyoming through an ASP model at the University of Pittsburgh, and through local installations in Taiwan, Canada, Mississippi, Michigan, California, and Texas. As of June 2006, about 20 regions with more than 200 healthcare facilities connected to RODS in real-time. It was also deployed during the 2002 Winter Olympics (Espino et al., 2004). It also serves as the user interface for national over-the-counter medication sales surveillance data collected through the NRDM.

Keywords

Recursive Least Square Open Source Project Syndromic Surveillance Recursive Least Square Algorithm Syndromic Surveillance System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Important readings:

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    Shen, Y., C. Adamou, J. N. Dowling, and G. F. Cooper (2008), “Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding,” Journal of Biomedical Informatics, 41, pp 224–231.CrossRefGoogle Scholar
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    Wallstrom, G. L., and W. R. Hogan (2007), “Unsupervised clustering of over-the-counter healthcare products into product categories,” Journal of Biomedical Informatics, 40(6), pp 642–648.PubMedCrossRefGoogle Scholar
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    Dara, J., J. N. Dowling, D. Travers, G. F. Cooper, and W. W. Chapman (2007), “Evaluation of preprocessing techniques for chief complaint classification,” Journal of Biomedical Informatics, 41(4), pp 613–623.Google Scholar
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    Espino, J. U., M. M. Wagner, F. C. Tsui, H. D. Su, R. T. Olszewski, Z. Lie, W. Chapman, X. Zeng, L. Ma, Z. W. Lu, and J. Dara (2004), “The RODS Open Source Project: removing a barrier to syndromic surveillance,” Medinfo, 11(Pt 2), pp 1192–1196.Google Scholar
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    Tsui, F. C., J. U. Espino, M. M. Wagner, P. Gesteland, O. Ivanov, R. T. Olszewski, Z. Liu, X. Zeng, W. Chapman, W. K. Wong, and A. Moore (2002), “Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System.” Proceedings of the AMIA Symposium, pp 815–819.Google Scholar

References

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Hsinchun Chen
    • 1
  • Daniel Zeng
    • 2
    • 3
  • Ping Yan
    • 2
  1. 1.Department of Management Information SystemsEller College of Management University of ArizonaTucsonUSA
  2. 2.Department of Management Information SystemsEller College of Management University of ArizonaTucsonUSA
  3. 3.Chinese Academy of SciencesBeijingChina

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