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Click on a topic under the Key Topic Areas section in the left column, then select a resource  from the list of resources that appear for that topic. You may also search for specific topics by entering one or more keywords in the Search bar. You can filter the search results by Content Type, Year, or Author Name.


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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 generally, early event detection systems. While the... Read more

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 detection algorithm that adapts to the time series... Read more

Content type: Abstract

Patient’s 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 characterize the variability of a gastrointestinal (GI)... Read more

Content type: Abstract

The existing New York State Department of Health emergency department syndromic surveillance system has used patient’s chief complaint (CC) for assigning to six syndrome categories (Respiratory, Fever, Gastrointestinal, Neurological, Rash, Asthma). The sensitivity and specificity of the CC... Read more

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 over time in the way the CC is recorded or because... Read more

Content type: Abstract

One limitation of syndromic surveillance systems based on emergency department (ED) data is the time and expense to investigate peak signals, especially when that involves phone calls or visits to the hospital. Many EDs use electronic medical records (EMRs) which are available remotely in real... Read more

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 (adapted from business research technology at AT&T Labs).... Read more

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 small outbreaks in low-incidence diseases. For example, when... Read more

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 expand the usefulness of syndromic surveillance, but little... Read more

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 diagnosis code and CC are available. A computer program calculates... Read more

Content type: Abstract


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