Title :
A new technique for survival prediction in trauma care using a neural network
Author :
McGonigal, Michael D.
Author_Institution :
Dept. of Surgery, St. Paul Ramsey Med. Center, MN, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
Artificial neural networks have been proven very effective at extrapolation and prediction. They are able to fit surface features to data points more closely than statistical regression. For these reasons, an artificial neural network was chosen as the basis for a new survival prediction system for traumatic injury in both children and adults. Traditional mathematical models for survival prediction in trauma care (TRISS and ASCOT) were compared to a new system based upon a multilayer perceptron neural network
Keywords :
medical computing; multilayer perceptrons; ASCOT; TRISS; adults; children; multilayer perceptron neural network; survival prediction; trauma care; Artificial neural networks; Humans; Injuries; Intelligent networks; Neural networks; Pediatrics; Physiology; Quality assurance; Surgery; Testing;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.414298
