AADPSAlgorithmic Architecture Data Processing System
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Methods: This report describes the application of data-mining techniques in developing an Automated Anomaly Detection Processor (AADP), which uses the Self Organizing Map clustering algorithm in conjunction with a Gaussian Mixture Model and a Bayesian Analyzer probabilistic model to detect anomalous occurrences in health data sets.
Results: AADP identified a simulated biologic terrorism attack occurring during the influenza season.
Conclusion: Development of AADP for biosurveillance adds a complementary method to extant surveillance systems and can improve real-time alerting so assets can be vectored for further epidemiologic investigation and early intervention.