Journal article
Optimizing A Syndromic Surveillance Text Classifier for Influenza-like Illness: Does Document Source Matter?
AMIA ... Annual Symposium proceedings, v 2008, pp 692-696
06 Nov 2008
PMID: 18999051
Abstract
Syndromic surveillance systems that incorporate electronic free-text data have primarily focused on extracting concepts of interest from chief complaint text, emergency department visit notes, and nurse triage notes. Due to availability and access, there has been limited work in the area of surveilling the full text of all electronic note documents compared with more specific document sources. This study provides an evaluation of the performance of a text classifier for detection of influenza-like illness (ILI) by document sources that are commonly used for biosurveillance by comparing them to routine visit notes, and a full electronic note corpus approach. Evaluating the performance of an automated text classifier for syndromic surveillance by source document will inform decisions regarding electronic textual data sources for potential use by automated biosurveillance systems. Even when a full electronic medical record is available, commonly available surveillance source documents provide acceptable statistical performance for automated ILI surveillance.
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Details
- Title
- Optimizing A Syndromic Surveillance Text Classifier for Influenza-like Illness: Does Document Source Matter?
- Creators
- Brett R. South - University of PittsburghWendy W. Chapman - University of PittsburghSylvain Delisle - University of PittsburghShuying Shen - University of PittsburghEricka Kalp - University of PittsburghTrish Perl - University of PittsburghMatthew H. Samore - University of PittsburghAdi V. Gundlapalli - University of Pittsburgh
- Publication Details
- AMIA ... Annual Symposium proceedings, v 2008, pp 692-696
- Publisher
- American Medical Informatics Association
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Scopus ID
- 2-s2.0-73949094912
- Other Identifier
- 991021899211204721