%0 Conference Paper %1 wong03bayesian %A Wong, Weng-Keen %A Moore, Andrew %A Cooper, Gregory %A Wagner, Michael %B Proceedings of the Twentieth International Conference on Machine Learning %C Menlo Park, California %D 2003 %E Fawcett, Tom %E Mishra, Nina %I AAAI Press %K AnomalyDetection %P 808-815 %T Bayesian Network Anomaly Pattern Detection for Disease Outbreaks %U http://www.autonlab.org/autonweb/papers/y2003/14642.html?branch=1&language=2 %X 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.

@inproceedings{wong03bayesian, abstract = {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.}, added-at = {2006-09-19T17:26:09.000+0200}, address = {Menlo Park, California}, author = {Wong, Weng-Keen and Moore, Andrew and Cooper, Gregory and Wagner, Michael}, biburl = {https://www.bibsonomy.org/bibtex/2596f6931fa250949156c815ffca0ceab/kaixo}, booktitle = {Proceedings of the Twentieth International Conference on Machine Learning}, description = {Anomaly Detection}, editor = {Fawcett, Tom and Mishra, Nina}, interhash = {1ebbdee5c265fa08b7eada27f83035bd}, intrahash = {596f6931fa250949156c815ffca0ceab}, key = {wong2003b}, keywords = {AnomalyDetection}, month = {August}, pages = {808-815}, publisher = {AAAI Press}, timestamp = {2006-09-19T17:26:09.000+0200}, title = {Bayesian Network Anomaly Pattern Detection for Disease Outbreaks}, url = {http://www.autonlab.org/autonweb/papers/y2003/14642.html?branch=1&language=2}, year = 2003 }