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Data Mining and Knowledge Discovery

, Volume 20, Issue 3, pp 328–360 | Cite as

A real-time temporal Bayesian architecture for event surveillance and its application to patient-specific multiple disease outbreak detection

  • Xia Jiang
  • Gregory F. Cooper
Article

Abstract

Reliable and accurate detection of disease outbreaks remains an important research topic in disease outbreak surveillance. A temporal surveillance system bases its analysis on data not only from the most recent time period, but also on data from previous time periods. A non-temporal system only looks at data from the most recent time period. There are two difficulties with a non-temporal system when it is used to monitor real data which often contain noise. First, it is prone to produce false positive signals during non-outbreak time periods. Second, during an outbreak, it tends to release false negative signals early in the outbreak, which can adversely affect the decision making process of the user of the system. We conjecture that by converting a non-temporal system to a temporal one, we may attenuate these difficulties inherent in a non-temporal system. In this paper, we propose a Bayesian network architecture for a class of temporal event surveillance models called BayesNet-T. Using this Bayesian network architecture, we can convert certain non-temporal surveillance systems to temporal ones. We apply this architecture to a previously developed non-temporal multiple-disease outbreak detection system called PC and create a temporal system called PCT. PCT takes Emergency Department (ED) patient chief complaint data as its input. The PCT system was constructed using both data (non-outbreak diseases) and expert assessments (outbreak diseases). We compare PCT to PC using a real influenza outbreak. Furthermore, we compare PCT to both PC and the classic statistical methods CUSUM and EWMA using a total of 240 influenza and Cryptosporidium disease outbreaks created by injecting stochastically simulated outbreak cases into real ED admission data. Our results indicate that PCT has a smaller mean time to detection than PC at low false alarm rates, and that PCT is more stable than PC in that once an outbreak is detected, PCT is better at maintaining the detection signal on future days.

Keywords

Temporal disease outbreak detection Bayesian network Patient-specific model Mining ED chief complaint data Uncertainty modeling Biosurveillance 

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Copyright information

© The Author(s) 2009

Authors and Affiliations

  1. 1.Department of Biomedical Informatics, School of MedicineUniversity of PittsburghPittsburghUSA

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