Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Statistics
  • Help & Advice
University of Warwick

The Library

  • Login

The use of a neural network for studying the relationship between air pollution and asthma-related emergency room visits

Tools
- Tools
+ Tools

UNSPECIFIED (1998) The use of a neural network for studying the relationship between air pollution and asthma-related emergency room visits. RESPIRATORY MEDICINE, 92 (10). pp. 1199-1202. ISSN 0954-6111

Full text not available from this repository.

Abstract

To establish the relationship between air pollution levels and bronchial asthma-associated emergency room (ER) visits,we adapted artificial network technology to conduct this study which focused on three different pollutants, sulphur dioxide, nitrogen oxide and ozone. The study population was comprised of adults presenting to the emergency room of a large metropolitan hospital in Israel during a 3-month period with acute exacerbation of bronchial asthma and who had a past history of intermittent airway disease compatible with bronchial asthma. The range of mean daily pollutants levels for the whole period were: O-3=15-26 mu g m(-3), NOx=36-108 mu g m(-3) NO = 16-70 mu g m(-3), and SO2 = 11-32 mu g m(-3). The data sets were composed of input air pollution levels and output ER visits. The first 126 data sets used for the training phase showed that maximal ER visits were mainly associated with the highest cumulative values of air pollution and mostly with nitrogen oxide. In phase two, an attempt was made to predict ER visits based on air pollution level in 49 data sets. The study findings demonstrated that ordinary network technology can be used for learning the effect of air pollution ER visits and, although limited in accuracy, to also predict future ER visits.

Item Type: Journal Article
Subjects: Q Science > QM Human anatomy
R Medicine > RC Internal medicine
Journal or Publication Title: RESPIRATORY MEDICINE
Publisher: W B SAUNDERS CO LTD
ISSN: 0954-6111
Date: October 1998
Volume: 92
Number: 10
Number of Pages: 4
Page Range: pp. 1199-1202
Publication Status: Published
URI: http://wrap.warwick.ac.uk/id/eprint/15216

Data sourced from Thomson Reuters' Web of Knowledge

Request changes to a record

Actions (login required)

View Item View Item
twitter

Email us: publications@warwick.ac.uk
Contact Details
About Us