Efficient Analytics for Effective Monitoring of Biomedical Security (2005)

Robin Sabhnani, Daniel B. Neill, Andrew W. Moore, Artur W. Dubrawski, Weng-Keen Wong

Tags

Biosurveillance, Spatial Scan, Tip Monitor, WSARE

Abstract

This paper reviews three successful statistical data
mining approaches developed recently at the Auton Lab of
Carnegie Mellon University to support public health officials in
their work towards protecting biomedical safety and security.
The presented methods focus on monitoring health care data
sources including hospital emergency department records, sales
of over-the-counter medications, and consumer food
complaints. Their purpose is to detect statistically significant
signs of disease outbreaks, or food safety related concerns, as
early as possible. These approaches have already been
successfully deployed in the United States and other developed
countries, but they also have a vast potential utility among
developing societies. The Auton Lab is actively seeking
additional deployments, and several pieces of the relevant
software are available for download and use free of charge.
This paper describes each of the presented methods, and
provides results of their utilization so far.

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Approximate BibTeX Entry

@inproceedings{BioMedICIA05,
    Month = {Dec},
    Year = {2005},
    Journal = {IEEE International Conference on Information and Automation, Sri Lanka},
    Author = { Robin Sabhnani, Daniel B. Neill, Andrew W. Moore, Artur W. Dubrawski, Weng-Keen Wong },
    Title = {Efficient Analytics for Effective Monitoring of Biomedical Security}
}