Rule-based Anomaly Pattern Detection for Detecting Disease Outbreaks
W. Wong, A. Moore, G. Cooper, and M. Wagner. Proceedings of the 18th National Conference on Artificial Intelligence, MIT Press, (2002)
Abstract
This paper presents an algorithm for performing early detection of disease outbreaks by searching a
database of emergency department cases for anomalous patterns. Traditional techniques for anomaly
detection are unsatisfactory for this problem because they identify individual data points that are
rare due to particular combinations of features. When applied to our scenario, these traditional
algorithms discover isolated outliers of particularly strange events, such as someone accidentally
shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect
anomalous patterns.
These patterns are groups with specific characteristics whose recent pattern of illness is anomalous
relative to historical patterns. We propose using a rule-based anomaly detection algorithm that
characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated
using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard
detection algorithm by measuring the number of false positives and the timeliness of detection.
Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for
evaluation. The results indicate that our algorithm has significantly better detection times for common
significance thresholds while having a slightly higher false positive rate.
%0 Conference Paper
%1 wong02rule
%A Wong, Weng-Keen
%A Moore, Andrew
%A Cooper, Gregory
%A Wagner, Michael
%B Proceedings of the 18th National Conference on Artificial Intelligence
%D 2002
%I MIT Press
%K AnomalyDetection
%T Rule-based Anomaly Pattern Detection for Detecting Disease Outbreaks
%U http://www.autonlab.org/autonweb/papers/y2002/14622.html
%X This paper presents an algorithm for performing early detection of disease outbreaks by searching a
database of emergency department cases for anomalous patterns. Traditional techniques for anomaly
detection are unsatisfactory for this problem because they identify individual data points that are
rare due to particular combinations of features. When applied to our scenario, these traditional
algorithms discover isolated outliers of particularly strange events, such as someone accidentally
shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect
anomalous patterns.
These patterns are groups with specific characteristics whose recent pattern of illness is anomalous
relative to historical patterns. We propose using a rule-based anomaly detection algorithm that
characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated
using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard
detection algorithm by measuring the number of false positives and the timeliness of detection.
Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for
evaluation. The results indicate that our algorithm has significantly better detection times for common
significance thresholds while having a slightly higher false positive rate.
@inproceedings{wong02rule,
abstract = {This paper presents an algorithm for performing early detection of disease outbreaks by searching a
database of emergency department cases for anomalous patterns. Traditional techniques for anomaly
detection are unsatisfactory for this problem because they identify individual data points that are
rare due to particular combinations of features. When applied to our scenario, these traditional
algorithms discover isolated outliers of particularly strange events, such as someone accidentally
shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect
anomalous patterns.
These patterns are groups with specific characteristics whose recent pattern of illness is anomalous
relative to historical patterns. We propose using a rule-based anomaly detection algorithm that
characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated
using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard
detection algorithm by measuring the number of false positives and the timeliness of detection.
Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for
evaluation. The results indicate that our algorithm has significantly better detection times for common
significance thresholds while having a slightly higher false positive rate.},
added-at = {2006-09-19T17:26:09.000+0200},
author = {Wong, Weng-Keen and Moore, Andrew and Cooper, Gregory and Wagner, Michael},
biburl = {https://www.bibsonomy.org/bibtex/224823e73be3308e92d966633fecb4f61/kaixo},
booktitle = {Proceedings of the 18th National Conference on Artificial Intelligence},
description = {Anomaly Detection},
interhash = {f4cf7d50183032204b0425b2eba6ae63},
intrahash = {24823e73be3308e92d966633fecb4f61},
keywords = {AnomalyDetection},
publisher = {MIT Press},
timestamp = {2006-09-19T17:26:09.000+0200},
title = {Rule-based Anomaly Pattern Detection for Detecting Disease Outbreaks},
url = {http://www.autonlab.org/autonweb/papers/y2002/14622.html},
year = 2002
}