Welcome to the Surveillance Knowledge Repository

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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 Syndromic surveillance systems often classify patients into syndromic categories based on emergency department (ED) chief complaints. There exists no standard set of syndromes for syndromic surveillance, and the available syndromic case definitions demonstrate substantial heterogeneity of... Read more

Content type: Abstract

To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance.

Content type: Abstract

We will convene a consultative meeting on chief complaint classifiers and standardized syndromic definitions in Pittsburgh, PA, from September 24-25,

Content type: Abstract

The Real-time Outbreak and Disease Surveillance system collects chief complaints as free text and uses a naïve Bayesian classifier called CoCo to classify the complaints into syndromic categories. CoCo 3.0 has been trained on 28,990 manually clas-sified chief complaints. The free text chief com-... Read more

Content type: Abstract

To understand the types of false positive cases identified by an Influenza-like illness (ILI) text classifier by measuring the prevalence of ILI-related concepts that are negated, hypothetical, include explicit mention of temporality, experienced by someone other than the patient, or described... Read more

Content type: Abstract

Case detection from chief complaints suffers from low to moderate sensitivity. Emergency Department (ED) reports contain detailed clinical information that could improve case detection ability and enhance outbreak characterization. We developed a text processing system called Topaz that could be... Read more

Content type: Abstract

There exists no standard set of syndromes for syndromic surveillance, and available syndromic case definitions demonstrate substantial heterogeneity of findings constituting the definition. Many syndromic case definitions require the presence of a syndromic finding (e.g., cough or diarrhea) and... Read more

Content type: Abstract

A major goal of biosurveillance is the timely detection of an infectious disease outbreak. Once a disease has been identified, another very important goal is to find all known cases of the disease to assist public health investigators. Natural language processing (NLP) systems may be able to... Read more

Content type: Abstract

The Extended Syndromic Surveillance Ontology (ESSO) is an open source terminological ontology designed to facilitate the text mining of clinical reports in English [1,2]. At the core of ESSO are 279 clinical concepts (for example, fever, confusion, headache, hallucination, fatigue) grouped into... Read more

Content type: Abstract

PyConTextKit is a web-based platform that extracts entities from clinical text and provides relevant metadata - for example, whether the entity is negated or hypothetical - using simple lexical clues occurring in the window of text surrounding the entity. The system provides a flexible framework... Read more

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National Syndromic
Surveillance Program


The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, Center for Disease Control and Prevention programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

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