This paper is in the following e-collection/theme issue:

Presentations, Abstracts, or Demonstrations (740)

Published on in Vol 7, No 1 (2015):

Identifying Emerging Novel Outbreaks In Textual Emergency Department Data

Identifying Emerging Novel Outbreaks In Textual Emergency Department Data

Identifying Emerging Novel Outbreaks In Textual Emergency Department Data

Authors of this article:

Mallory Nobles ;   Lana Deyneka ;   Amy Ising ;   Daniel B. Neill
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    We apply a novel semantic scan statistic approach to solve a problem posed by the NC DETECT team, North Carolina Division of Public Health (NC DPH) and UNC Department of Emergency Medicine Carolina Center for Health Informatics, and facilitated by the ISDS Technical Conventions Committee. This use case identifies a need for methodology that detects emerging, potentially novel outbreaks in free-text emergency department (ED) chief complaint data. Our semantic scan approach successfully addresses this problem, eliminates the need for classifying cases into pre-defined syndromes and identifies emerging clusters that public health officials could not have predicted in advance.