Bayesian Network Anomaly Pattern Detection for Disease Outbreaks
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks
复制标题
DOI：
--
复制
发表时间：
2003-08
期刊：
影响因子：
--
通讯作者：
Weng-Keen Wong;A. Moore;G. Cooper;M. Wagner
中科院分区：
文献类型：
--
作者：
Weng-Keen Wong;A. Moore;G. Cooper;M. Wagner
Early disease outbreak detection systems typically monitor health care data for irregularities by comparing the distribution of recent data against a baseline distribution. Determining the baseline is difficult due to the presence of different trends in health care data, such as trends caused by the day of week and by seasonal variations in temperature and weather. Creating the baseline distribution without taking these trends into account can lead to unacceptably high false positive counts and slow detection times. This paper replaces the baseline method of (Wong et al., 2002) with a Bayesian network which produces the baseline distribution by taking the joint distribution of the data and conditioning on attributes that are responsible for the trends. We show that our algorithm, called 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. We also include the results of running WSARE 3.0 on real Emergency Department data
