Bayesian information fusion networks for biosurveillance applications.
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.
Mnatsakanyan
Zaruhi R
ZR
The Johns Hopkins University Applied Physics Laboratory (JHU/APL), 11100 Johns Hopkins Road, Laurel, MD 20723, USA.
Burkom
Howard S
HS
Coberly
Jacqueline S
JS
Lombardo
Joseph S
JS
D000465:Algorithms; D001499:Bayes Theorem; D057225:Data Mining / Q000379:methods*; D003661:Decision Support Techniques; D004196:Disease Outbreaks / Q000517:prevention & control*; D004219:District of Columbia / Q000453:epidemiology; D006305:Health Status Indicators; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000517:prevention & control*; D008396:Maryland / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D014768:Virginia / Q000453:epidemiology