Displaying results 1 - 4 of 4
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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… records that are anomalous with respect to the historical dis- tribution of data. However, disease outbreaks typi- … County, PA. Figure 1 plots the detection preci- sion, i.e. the proportion of detected anomalies that were true … The Bayesian aerosol release detector. Stat. Med., 2007, 26: 5225-5252. Further Information: Daniel B. Neill, … -
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… of values along each categorical dimension. As evaluating all possible outbreak hypotheses can be computationally … of values along each categorical dimension. As evaluating all possible outbreak hypotheses can be computationally … Reports3. The data stores patient visits spanning 26 regions and 9 diseases reported over 2.5 years. We … -
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… syndromic systems only monitor state level aggregates for all syndromes or a few combinations of syndromes, gender and … symp- tom in male adults is indicative of a particular dis- ease). Based on the past experience public health … dromic systems only monitor state level aggregates for all syndromes or a few combinations of syn- dromes, gender … -
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.… indicate that Fisher’s aggregation substantially improves de- tection power (timeliness as well as alert frequency) … LNCS Vol. 4506, Springer-Verlag, 2007. [3] L. Wasserman. All of Statistics. Springer, 2004. [4] A. Dubrawski, K. …