Displaying results 1 - 4 of 4
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Detection of multiple overlapping anomalous clusters in categorical data
Content Type: Abstract
Syndromic surveillance typically involves collecting time-stamped transactional data, such as patient triage or examination records or pharmacy sales. Such records usually span multiple categorical features, such as location, age… read more… definitions or focus only on detecting spatially co-located clusters for disease outbreak detection. Further, … definitions1 or focus only on detecting spatially co-located clusters2 for disease outbreak detection. … et al.; licensee Emerging Health Threats Journal. www.eht-journal.org 5555 algorithms are allowed to generate … -
Anomaly Pattern Detection for Biosurveillance
Content Type: Abstract
We propose a new method for detecting patterns of disease cases that correspond to emerging outbreaks. Our Anomaly Pattern Detector (APD) first uses a "local anomaly detector" to identify individually anomalous records and then searches over subsets… read more… GI). In order to determine if the subset of the test data cor- responding to rule R has an unexpectedly high con- centration of anomalies, we compare it to the corres- … Further Information: Daniel B. Neill, neill@cs.cmu.edu www.cs.cmu.edu/~neill and www.autonlab.org Advances in … -
Multivariate Time Series Analyses Using Primitive Univariate Algorithms
Content Type: Abstract
Time series analysis is very popular in syndromic surveillance. Mostly, public health officials track in the order of hundreds of disease models or univariate time series daily looking for signals of disease outbreaks. These time series can be… read more… a set of disease models (for e.g. fever or headache symp- tom in male adults is indicative of a particular dis- ease). … age-groups. But most real world disease models are more com- plex and affect multiple syndromes, or multiple age- … -
Learning Specific Detectors of Adverse Events in Multivariate Time Series
Content Type: Abstract
This paper describes how powerful detectors of adverse events manifested in multivariate series of bio-surveillance data can be learned using only a few labeled instances of such events.… by being able to raise an alert even if none of the com- ponent signals is critical, but if some of them are …