Displaying results 1 - 8 of 11
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A System for Simulation: Introducing Outbreaks into Time Series Data
Content Type: Abstract
Objective Several authors have described ways to introduce artificial outbreaks into time series for the purpose of developing, testing, and evaluating the effectiveness and timeliness of anomaly detection algorithms, and more… read more -
A Case Manager Tool for Anomaly Investigation in BioSurveillance
Content Type: Abstract
Effective anomaly detection depends on the timely, asynchronous generation of anomalies from multiple data streams using multiple algorithms. Our objective is to describe the use of a case manager tool for combining anomalies into cases, and for… read more -
An Adaptive Anomaly Detection Algorithm
Content Type: Abstract
Ideal anomaly detection algorithms shoulddetect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. The algorithms should also be easy to use. Our objective was to develop an anomaly… read more -
Optimizing Performance of an Ngram Method for Classifying Emergency Department Visits into the Respiratory Syndrome
Content Type: Abstract
A number of different methods are currently used to classify patients into syndromic groups based on the patient’s chief complaint (CC). We previously reported results using an “Ngram” text processing program for building classifiers… read more -
Performance of an Adaptive Anomaly Detection Algorithm for a Low Incidence Syndrome Before and After a Major Outbreak
Content Type: Abstract
Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Further, the algorithm needs to perform well when the need is to detect… read more -
HWR at the Contest: A Holt-Winters-Based Method Applied to Simulated Outbreaks
Content Type: Abstract
Objective: Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Our objective was to develop an anomaly detection algorithm that adapts to the… read more -
Improvement in Performance of Ngram Classifiers with Frequent Updates
Content Type: Abstract
Syndromic surveillance of emergency department(ED) visit data is often based on computerized classifiers which assign patient chief complaints (CC) tosyndromes. These classifiers may need to be updatedperiodically to account for changes… read more -
Sensitivity and Specificity of an Ngram Method for Classifying Emergency Department Visits into the Respiratory Syndrome in the Turkish Language
Content Type: Abstract
Previously we developed an “Ngram” classifier for syndromic surveillance of emergency department (ED) chief complaints (CC) in Turkish for bioterrorism. The classifier is developed from a set of ED visits for which both the ICD… read more