Displaying results 1 - 8 of 19
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A Pilot Study of Aberration Detection Algorithms with Simulated Data
Content Type: Abstract
To evaluate four algorithms with varying baseline periods and adjustment for day of week for anomaly detection in syndromic surveillance data. read more -
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 -
Components of Inter-Hospital Variability in Chief Complaints Assigned to a Gastrointestinal Syndrome
Content Type: Abstract
Patientâs chief complaint (CC) is often used for syndromic surveillance for bioterrorism and outbreak detection, but little is known about the inter-hospital variability in the sensitivity of this method. Objective: Our objective was to… 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 -
Performance of Sub-Syndrome Chief Complaint Classifiers for the GI Syndrome
Content Type: Abstract
The Centers for Disease Control and Prevention BioSense project has developed chief complaint (CC) and ICD9 sub-syndrome classifiers for the major syndromes for early event detection and situational awareness. This has the potential to… read more