DocumentCode :
2400137
Title :
Neurocomputing applications in post-operative liver transplant monitoring
Author :
Melvin, D.G. ; Niranjan, M. ; Prager, R.W. ; Trull, A.K. ; Hughes, V.F.
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
fYear :
1997
fDate :
24-26 Sep 1997
Firstpage :
216
Lastpage :
225
Abstract :
This paper explores the potential for the application of neurocomputing technology to the domain of post-operative liver transplant monitoring. The investigation compares a neural network model with two classical statistical techniques using biochemical information obtained from a set of liver transplant patients. Each approach combines the results of a number of liver function tests to predict the presence of allograft rejection. Each system is assessed, relative to the clinical gold standard, in terms of its overall accuracy and degree of advance warning offered. Applying nonlinear methods does offer an advantage over the traditional linear techniques. The underlying structure of the data set has also been determined using k-means cluster analysis. This analysis suggests important directions for future investigation including the use of temporal information. Preliminary results of incorporating this temporal information are also presented
Keywords :
liver; medical computing; multilayer perceptrons; patient monitoring; pattern classification; surgery; allograft rejection; k-means cluster analysis; monitoring; multilayer perceptron; neural network model; neurocomputing; pattern classification; post-operative liver transplant; temporal information; Biochemistry; Drugs; Gold; Immune system; Information analysis; Liver; Medical treatment; Neural networks; Patient monitoring; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Conference_Location :
Amelia Island, FL
ISSN :
1089-3555
Print_ISBN :
0-7803-4256-9
Type :
conf
DOI :
10.1109/NNSP.1997.622401
Filename :
622401
Link To Document :
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