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Detection of disease outbreaks based...

Mahmoud, El Sayed A.

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Detection of disease outbreaks based on emergency department and telehealth data using artificial neural networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Detection of disease outbreaks based on emergency department and telehealth data using artificial neural networks./
Author:	Mahmoud, El Sayed A.
Description:	99 p.
Notes:	Source: Masters Abstracts International, Volume: 46-04, page: 2162.
Contained By:	Masters Abstracts International46-04.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR36543
ISBN:	9780494365434

Detection of disease outbreaks based on emergency department and telehealth data using artificial neural networks.
Mahmoud, El Sayed A.

Detection of disease outbreaks based on emergency department and telehealth data using artificial neural networks. - 99 p.

Source: Masters Abstracts International, Volume: 46-04, page: 2162.

Thesis (M.Sc.)--University of Guelph (Canada), 2008.

Syndromic Surveillance protects the public by detecting disease outbreaks early. Public health officials are interested in methods that will provide earlier and more accurate detections. Back Propagation has been demonstrated to be an accurate, robust, and scalable detection technique for disease outbreaks in over-the-counter pharmaceutical sales, and Support Vector Machines have been proven to be valuable in prediction. The purpose of this study was to determine whether Support Vector Machines are comparable to Back Propagation in the context of Syndromic Surveillance. This study used Back Propagation and Support Vector Machines to detect outbreaks based on Emergency Department and Telehealth data. We utilized a data simulation methodology to produce sufficient quantities of realistic data to perform this study. The results demonstrated that Support Vector Machines with polynomial kernel are superior to Back Propagation for detecting disease outbreaks based on data from Emergency Department. In addition, Support Vector Machines with linear kernel are comparable to Back Propagation for detecting outbreaks based on data from Telehealth.

ISBN: 9780494365434Subjects--Topical Terms:

769149
Artificial Intelligence.

Detection of disease outbreaks based on emergency department and telehealth data using artificial neural networks.
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100 1 $a Mahmoud, El Sayed A. $3 1282514
245 1 0 $a Detection of disease outbreaks based on emergency department and telehealth data using artificial neural networks.
300 $a 99 p.
500 $a Source: Masters Abstracts International, Volume: 46-04, page: 2162.
502 $a Thesis (M.Sc.)--University of Guelph (Canada), 2008.
520 $a Syndromic Surveillance protects the public by detecting disease outbreaks early. Public health officials are interested in methods that will provide earlier and more accurate detections. Back Propagation has been demonstrated to be an accurate, robust, and scalable detection technique for disease outbreaks in over-the-counter pharmaceutical sales, and Support Vector Machines have been proven to be valuable in prediction. The purpose of this study was to determine whether Support Vector Machines are comparable to Back Propagation in the context of Syndromic Surveillance. This study used Back Propagation and Support Vector Machines to detect outbreaks based on Emergency Department and Telehealth data. We utilized a data simulation methodology to produce sufficient quantities of realistic data to perform this study. The results demonstrated that Support Vector Machines with polynomial kernel are superior to Back Propagation for detecting disease outbreaks based on data from Emergency Department. In addition, Support Vector Machines with linear kernel are comparable to Back Propagation for detecting outbreaks based on data from Telehealth.
590 $a School code: 0081.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
650 4 $a Health Sciences, Epidemiology. $3 1019544
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