Data normalization in biosurveillance: an information-theoretic approach.

William Peter, Amir Najmi, Howard Burkom

Research output: Contribution to journalArticle

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
Publication statusPublished - Dec 1 2007

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ASJC Scopus subject areas

  • Medicine(all)

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