New directions in artificial intelligence for public health surveillance

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number6163563
Pages (from-to)56-59
Number of pages4
JournalIEEE Intelligent Systems
Volume27
Issue number1
DOIs
StatePublished - Jan 1 2012

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Public health
Artificial intelligence
Health

Keywords

  • disease surveillance
  • event detection
  • public health surveillance
  • semantic scan statistic
  • spatial and subset scanning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

Cite this

New directions in artificial intelligence for public health surveillance. / Neill, Daniel.

In: IEEE Intelligent Systems, Vol. 27, No. 1, 6163563, 01.01.2012, p. 56-59.

Research output: Contribution to journalArticle

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