Displaying results 1 - 5 of 5
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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 -
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. -
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 -
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 -
Searching for Complex Patterns Using Disjunctive Anomaly Detection
Content Type: Abstract
Modern biosurveillance data contains thousands of unique time series defined across various categorical dimensions (zipcode, age groups, hospitals). Many algorithms are overly specific (tracking each time series independently would often miss early… read more