國立虎尾科技大學 |

Language: English

Back to Search results for [ subject:"Artificial neural network" ]

An integrated decision tree-artifici...

Yu, Nicole.

An integrated decision tree-artificial neural network hybrid to estimate clinical outcomes: ICU mortality and pre-term birth.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An integrated decision tree-artificial neural network hybrid to estimate clinical outcomes: ICU mortality and pre-term birth./
Author:	Yu, Nicole.
Description:	182 p.
Notes:	Source: Masters Abstracts International, Volume: 48-06, page: 3748.
Contained By:	Masters Abstracts International48-06.
Subject:	Engineering, Biomedical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR63825
ISBN:	9780494638255

An integrated decision tree-artificial neural network hybrid to estimate clinical outcomes: ICU mortality and pre-term birth.
Yu, Nicole.

An integrated decision tree-artificial neural network hybrid to estimate clinical outcomes: ICU mortality and pre-term birth. - 182 p.

Source: Masters Abstracts International, Volume: 48-06, page: 3748.

Thesis (M.A.Sc.)--Carleton University (Canada), 2010.

Engineering and designing an artificial intelligence tool for pattern classification has promising use in the field of medicine. The data mining approach integrates Decision Trees (DTs); Artificial Neural Networks (ANNs), specifically a Classification-based Multi-Layer Perceptron (MLP); and a MLP ANN with risk stratification. This tool predicted mortality in an adult intensive care unit: sensitivity of 75.0%, specificity of 89.54% and an area under the curve (AUC) of 0.9417 for postoperative cases; non-postoperative cases had a sensitivity of 90.90%, specificity 75.16% and an AUC of 0.8333. Prediction of high-risk preterm birth had a sensitivity of 65.13%, specificity of 84.07% and an AUC of 0.8195 for Parous cases and a sensitivity of 61.08%, specificity of 71.14%, and an AUC of 0.7195 for Nulli-parous cases. The trained integrated tool reduced the complexity while yielding prediction accuracies that exceed current results in the literature.

ISBN: 9780494638255Subjects--Topical Terms:

845403
Engineering, Biomedical.

An integrated decision tree-artificial neural network hybrid to estimate clinical outcomes: ICU mortality and pre-term birth.
LDR:01816nam 2200265 4500 001 713010
005 20121003100340.5
008 121101s2010 ||||||||||||||||| ||eng d
020 $a 9780494638255
035 $a (UMI)AAIMR63825
035 $a AAIMR63825
040 $a UMI $c UMI
100 1 $a Yu, Nicole. $3 845596
245 1 3 $a An integrated decision tree-artificial neural network hybrid to estimate clinical outcomes: ICU mortality and pre-term birth.
300 $a 182 p.
500 $a Source: Masters Abstracts International, Volume: 48-06, page: 3748.
502 $a Thesis (M.A.Sc.)--Carleton University (Canada), 2010.
520 $a Engineering and designing an artificial intelligence tool for pattern classification has promising use in the field of medicine. The data mining approach integrates Decision Trees (DTs); Artificial Neural Networks (ANNs), specifically a Classification-based Multi-Layer Perceptron (MLP); and a MLP ANN with risk stratification. This tool predicted mortality in an adult intensive care unit: sensitivity of 75.0%, specificity of 89.54% and an area under the curve (AUC) of 0.9417 for postoperative cases; non-postoperative cases had a sensitivity of 90.90%, specificity 75.16% and an AUC of 0.8333. Prediction of high-risk preterm birth had a sensitivity of 65.13%, specificity of 84.07% and an AUC of 0.8195 for Parous cases and a sensitivity of 61.08%, specificity of 71.14%, and an AUC of 0.7195 for Nulli-parous cases. The trained integrated tool reduced the complexity while yielding prediction accuracies that exceed current results in the literature.
590 $a School code: 0040.
650 4 $a Engineering, Biomedical. $3 845403
650 4 $a Health Sciences, Medicine and Surgery. $3 845598
650 4 $a Artificial Intelligence. $3 646849
690 $a 0541
690 $a 0564
690 $a 0800
710 2 $a Carleton University (Canada). $3 845597
773 0 $t Masters Abstracts International $g 48-06.
790 $a 0040
791 $a M.A.Sc.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR63825

based on 0 review(s)

Multimedia

Reviews

Add a review and share your thoughts with other readers

Export

pickup library