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Multicenter Study
. 2009 Nov-Dec;16(6):855-63.
doi: 10.1197/jamia.M2647. Epub 2009 Aug 28.

Bayesian information fusion networks for biosurveillance applications

Affiliations
Multicenter Study

Bayesian information fusion networks for biosurveillance applications

Zaruhi R Mnatsakanyan et al. J Am Med Inform Assoc. 2009 Nov-Dec.

Abstract

This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time offsets between events in the multiple data streams were established. The Bayesian Network was built to fuse data from multiple sources and identify influenza-like epidemiologically relevant events. Results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments. Further research should be done to investigate correlations between data sources for efficient fusion of the collected data.

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Figures

Figure 1
Figure 1
Multilevel information processing diagram.
Figure 2
Figure 2
Influenza detection BN structure.
Figure 3
Figure 3
Laboratory-confirmed cases of influenza in the State of Maryland.
Figure 4
Figure 4
Influenza-like illnesses (ILI) reported by Sentinel Physicians in Virginia by region, during 2005-06 influenza season (from Virginia Health Department Influenza Surveillance Annual Report).
Figure 5
Figure 5
Output from system indicating probabilities of influenza in Montgomery County between June 2003 and May 2006.
Figure 8
Figure 8
Probabilities (blue) and ESSENCE Warnings and Alerts (red) for Fairfax County, Virginia.

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Publication types