Planning and acting in partially observable stochastic domains
Artificial Intelligence
Data mining for early disease outbreak detection
Data mining for early disease outbreak detection
Optimizing anthrax outbreak detection using reinforcement learning
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Establishing fraud detection patterns based on signatures
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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Surveillance systems have long been used to monitor industrial processes and are becoming increasingly popular in public health and anti-terrorism applications. Most early detection systems produce a time series of p-values or some other statistic as their output. Typically, the decision to signal an alarm is based on a threshold or other simple algorithm such as CUSUM that accumulates detection information temporally.We formulate a POMDP model of underlying events and observations from a detector. We solve the model and show how it is used for single-output detectors. When dealing with spatio-temporal data, scan statistics are a popular method of building detectors. We describe the use of scan statistics in surveillance and how our POMDP model can be used to perform alarm signaling with them. We compare the results obtained by our method with simple thresholding and CUSUM on synthetic and semi-synthetic health data.