Rule-based anomaly pattern detection for detecting disease outbreaks
Wong WK, Moore AM, Cooper GF, Wagner MM. Rule-based anomaly pattern detection for detecting disease outbreaks. In: Proceedings of National Conference on Artificial Intelligence (AAAI) (2002) 217-223.
This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditionaltechniquesforanomalydetectionareunsatisfactory 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 rulebased anomaly detection algorithm that characterizes each anomalous pattern with a rule. The signiﬁcance of each rule is carefully evaluated using Fisher’s Exact Testandarandomizationtest.Ouralgorithmiscompared 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.