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New Directions in Artificial Intelligence for Public Health Surveillance
January/February 2012 (vol. 27 no. 1)
pp. 56-59
Daniel B. Neill, Event and Pattern Detection Laboratory, H.J. Heinz III College, Carnegie Mellon University

The next decade of disease surveillance research will require novel methods to effectively use massive quantities of complex, high-dimensional data. We summarize two recent approaches which deal with the increasing complexity and scale of health data, including the use of rich text data to detect emerging outbreaks with novel symptom patterns, and fast subset scan methods to efficiently identify the most relevant patterns in massive datasets.

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Index Terms:
event detection; disease surveillance; public health surveillance; spatial and subset scanning; semantic scan statistic
Daniel B. Neill, "New Directions in Artificial Intelligence for Public Health Surveillance," IEEE Intelligent Systems, vol. 27, no. 1, pp. 56-59, Jan.-Feb. 2012, doi:10.1109/MIS.2012.18
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