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What's Strange About Recent Events, Version 3.0 --- Accounting
for a Changing Baseline
Weng-Keen Wong,1 A.
Moore,1 G. Cooper,2 M.
Wagner2
1Carnegie Mellon University, Pittsburgh, Pennsylvania; 2University of Pittsburgh, Pittsburgh, Pennsylvania
Corresponding author: Weng-Keen Wong, University of Pittsburgh, 200 Lothrop St., 8084 Forbes Tower, Pittsburgh, PA 15213. Telephone: 412-246-5824; Fax: 412-246-5985; E-mail:
wwong@cbmi.pitt.edu.
Abstract
Introduction: This paper extends the algorithm outlined in an earlier version of this paper by detecting
anomalous patterns in health-care data while accounting for temporal trends in the data (e.g., fluctuations caused by
day-of-week effects and seasonal variations in temperature and weather).
Objectives: What's Strange About Recent Events (WSARE) 2.0 compared the distribution of recent data against
a baseline distribution obtained from raw historic data. However, this baseline is affected by different fluctuations
in the data (e.g., day-of-week effects and seasonal variations). Creating the baseline distribution without taking
such trends into account can lead to unacceptably high false-positive counts and slow detection times.
Methods: This paper replaces the baseline method of WSARE 2.0 with a Bayesian network, which produces
the baseline distribution by taking the joint probability distribution of the data and conditioning on attributes that are responsible for the trends.
Results: WSARE 3.0 is evaluated on a simulator that contains different temporal trends. Annotated results on
real emergency department data are also included.
Conclusions: WSARE 3.0 is able to detect outbreaks in simulated data with almost the earliest possible
detection time while keeping a low false-positive count.
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