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Presented January 28, 2008

The following presentation was given as part of the Integrated Surveillance Seminar Series from the National Center for Public Health Informatics and provides an overview of public health informatics translational research including how to identify requirements... Read more

Content type: Presentation Slides

In surveillance it is imperative that we know when and where a disease first begins. The objective of this study was to examine trends in traveling waves of influenza in the US elderly population. Preparedness for influenza is an important yet difficult public health goal due to variability in... Read more

Content type: Abstract

Managers of the NC DETECT[1] surveillance system wanted to augment standard tabular Web-based access with a Web-based spatial-temporal interface to allow users to see spatial and temporal characteristics of the surveillance data. Users need to see spatial and temporal patterns in the data to... Read more

Content type: Abstract

The CDC recently developed sub-syndromes for classifying disease to enhance syndromic surveillance of natural outbreaks and bioterrorism. They have developed ICD9 classifiers for six GI Illness subsyndromes: Abdominal Pain, Nausea and Vomiting (N&V), Diarrhea, Anorexia, Intestinal infections... Read more

Content type: Abstract

This paper highlights the use of syndromic surveillance data to examine daily trends in emergency department (ED) volume at an urban public hospital.

Content type: Abstract

We evaluated emergency department (ED) and emergency medical services (EMS) data for describing an outbreak of carbon monoxide (CO) poisoning following a windstorm, and determined whether loss of power was followed by an increase in other health conditions.

Content type: Abstract

To quantify the positive predictive values of ICD-9 CM diagnosis codes for public health surveillance of communicable diseases.

Content type: Abstract

This paper describes and applies a new method for identifying biosurveillance search terms using the Semantic NetworkÆ of the Unified Medical Language SystemÆ (UMLSÆ)[1].

Content type: Abstract

This paper describes the application of syndromic surveillance data from area school districts to detect influenza epidemics in a county setting.

Content type: Abstract

To identify epidemiologically important factors such as infectious disease exposure history, travel or specific variables from unstructured data using natural language processing (NLP) methods.

Content type: Abstract


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