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Fever detection from free-text clinical records for biosurveillance
Automatic detection of cases of febrile illness may have potential for early detection of outbreaks of infectious disease either by identification of anomalous numbers of febrile illness or in concert with other information in diagnosing specific syndromes, such as febrile respiratory syndrome. At m...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2004
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7128853/ https://www.ncbi.nlm.nih.gov/pubmed/15120658 http://dx.doi.org/10.1016/j.jbi.2004.03.002 |
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author | Chapman, Wendy W Dowling, John N Wagner, Michael M |
author_facet | Chapman, Wendy W Dowling, John N Wagner, Michael M |
author_sort | Chapman, Wendy W |
collection | PubMed |
description | Automatic detection of cases of febrile illness may have potential for early detection of outbreaks of infectious disease either by identification of anomalous numbers of febrile illness or in concert with other information in diagnosing specific syndromes, such as febrile respiratory syndrome. At most institutions, febrile information is contained only in free-text clinical records. We compared the sensitivity and specificity of three fever detection algorithms for detecting fever from free-text. Keyword CC and CoCo classified patients based on triage chief complaints; Keyword HP classified patients based on dictated emergency department reports. Keyword HP was the most sensitive (sensitivity 0.98, specificity 0.89), and Keyword CC was the most specific (sensitivity 0.61, specificity 1.0). Because chief complaints are available sooner than emergency department reports, we suggest a combined application that classifies patients based on their chief complaint followed by classification based on their emergency department report, once the report becomes available. |
format | Online Article Text |
id | pubmed-7128853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2004 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71288532020-04-08 Fever detection from free-text clinical records for biosurveillance Chapman, Wendy W Dowling, John N Wagner, Michael M J Biomed Inform Article Automatic detection of cases of febrile illness may have potential for early detection of outbreaks of infectious disease either by identification of anomalous numbers of febrile illness or in concert with other information in diagnosing specific syndromes, such as febrile respiratory syndrome. At most institutions, febrile information is contained only in free-text clinical records. We compared the sensitivity and specificity of three fever detection algorithms for detecting fever from free-text. Keyword CC and CoCo classified patients based on triage chief complaints; Keyword HP classified patients based on dictated emergency department reports. Keyword HP was the most sensitive (sensitivity 0.98, specificity 0.89), and Keyword CC was the most specific (sensitivity 0.61, specificity 1.0). Because chief complaints are available sooner than emergency department reports, we suggest a combined application that classifies patients based on their chief complaint followed by classification based on their emergency department report, once the report becomes available. Elsevier Inc. 2004-04 2004-04-10 /pmc/articles/PMC7128853/ /pubmed/15120658 http://dx.doi.org/10.1016/j.jbi.2004.03.002 Text en Copyright © 2004 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Chapman, Wendy W Dowling, John N Wagner, Michael M Fever detection from free-text clinical records for biosurveillance |
title | Fever detection from free-text clinical records for biosurveillance |
title_full | Fever detection from free-text clinical records for biosurveillance |
title_fullStr | Fever detection from free-text clinical records for biosurveillance |
title_full_unstemmed | Fever detection from free-text clinical records for biosurveillance |
title_short | Fever detection from free-text clinical records for biosurveillance |
title_sort | fever detection from free-text clinical records for biosurveillance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7128853/ https://www.ncbi.nlm.nih.gov/pubmed/15120658 http://dx.doi.org/10.1016/j.jbi.2004.03.002 |
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