DocumentCode :
3137864
Title :
Using Artificial Intelligence to Estimate Outcomes in Perinatal Medicine
Author :
Frize, Monique ; Ibrahim, Doaa ; Catley, Christina ; Walker, Robin C.
Author_Institution :
Eng. Syst. & Comput. Eng., Carleton Univ., Ottawa, Ont.
fYear :
2006
fDate :
38838
Firstpage :
730
Lastpage :
733
Abstract :
A combination of tools that include artificial neural networks (ANNs) and case-based reasoning (CBR) allow the development of prediction models that have the potential to help physicians in their tasks of making a diagnosis and deciding on a course of therapy. The models developed by our research group to date predict the occurrence of pre-term births, delivery type, and Apgar score. Future work will include testing the prototypes in a clinical setting. Such systems have the potential to add value to current clinical tools and improve predictions and the management of patients for better clinical outcomes
Keywords :
case-based reasoning; medical diagnostic computing; neural nets; paediatrics; patient diagnosis; patient treatment; artificial intelligence; artificial neural network; case-based reasoning; clinical tool; medical therapy; patient diagnosis; perinatal medicine; Artificial intelligence; Artificial neural networks; Fetus; Medical diagnostic imaging; Medical services; Medical treatment; Pediatrics; Physics computing; Power engineering computing; Predictive models; artificial neural networks; perinatal outcomes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering, 2006. CCECE '06. Canadian Conference on
Conference_Location :
Ottawa, Ont.
Print_ISBN :
1-4244-0038-4
Electronic_ISBN :
1-4244-0038-4
Type :
conf
DOI :
10.1109/CCECE.2006.277807
Filename :
4054736
Link To Document :
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