Monday
March 6th
4:00 - 4:50pm
KEC 1001

Weng-Keen Wong
Assistant Professor
School of EECS
Oregon State University


PANDA: A Bayesian biosurveillance algorithm for detecting a large-scale
anthrax attack


Abstract Syndromic surveillance is a field that involves the collection
and analysis of data that precede diagnosis.  The goal of syndromic
surveillance is the early detection of both naturally occurring and
bioterrorist-induced disease outbreaks.  The scenario that is of
greatest concern to syndromic surveillance systems is a large-scale
release of inhalational anthrax.  Data mining algorithms play a critical
role in analysis of syndromic surveillance data by detecting anomalies
that correspond to disease outbreaks.  This talk will present the
Population-wide Anomaly Detection and Assessment (PANDA) algorithm,
which is specifically designed to monitor health-care data for the onset
of an outbreak caused by an outdoor, airborne release of inhalational
anthrax. At the heart of the PANDA algorithm is a causal Bayesian
network which models the spatio-temporal effects of the outbreak on a
population.  The most unique aspect of the PANDA algorithm is its
attempt to model every individual in the population under surveillance.

Biography:

Weng-Keen Wong is an Assistant Professor of Computer Science at Oregon
State University.  He received his Ph.D. (2004) and M.S. (2001) in
Computer Science at Carnegie Mellon University, and his B.Sc. (1997)
from the University of British Columbia.  His research areas are in data
mining and machine learning, with specific interests in anomaly
detection, surveillance algorithms, and mining large scale datasets. His
Ph.D. thesis was entitled "Data Mining Algorithms for the Early
Detection of Disease Outbreaks" and he is involved in the field of
disease outbreak surveillance.

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