Data normalization in biosurveillance: an information-theoretic approach.

William Peter, Amir H. Najmi, Howard Burkom

Research output: Contribution to journalArticlepeer-review

Abstract

An approach to identifying public health threats by characterizing syndromic surveillance data in terms of its surprisability is discussed. Surprisability in our model is measured by assigning a probability distribution to a time series, and then calculating its entropy, leading to a straightforward designation of an alert. Initial application of our method is to investigate the applicability of using suitably-normalized syndromic counts (i.e., proportions) to improve early event detection.

Original languageEnglish (US)
Number of pages1
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2007

ASJC Scopus subject areas

  • Medicine(all)

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