東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Detection of disease outbreaks based...

Mahmoud, El Sayed A.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Detection of disease outbreaks based on emergency department and telehealth data using artificial neural networks./
作者:	Mahmoud, El Sayed A.
面頁冊數:	99 p.
附註:	Source: Masters Abstracts International, Volume: 46-04, page: 2162.
Contained By:	Masters Abstracts International46-04.
標題:	Artificial Intelligence. -
電子資源:	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.
LDR:01990nam 2200265 a 45 001 959046
005 20110704
008 110704s2008 ||||||||||||||||| ||eng d
020 $a 9780494365434
035 $a (UMI)AAIMR36543
035 $a AAIMR36543
040 $a UMI $c UMI
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
690 $a 0766
690 $a 0800
690 $a 0984
710 2 $a University of Guelph (Canada). $3 1018650
773 0 $t Masters Abstracts International $g 46-04.
790 $a 0081
791 $a M.Sc.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR36543