Predicting cytomegalovirus disease after renal transplantation: an artificial neural network approach.
Outcome prediction is becoming increasingly important in medicine, but when a resource is scarce the need for accurate prediction becomes acute. Prediction based on biostatistical models has been in use for many years, but in areas such as renal transplantation their results have been disappointing. Recently however, there has been growing interest in the use of artificial neural networks for prediction. The creation of a large database containing high quality data on renal transplantation patients in Wales offers an ideal opportunity to research a new area viz., the ability of these techniques to accurately predict outcomes such as the appearance of disease in transplant recipients or the time to graft failure. This paper describes the use of neural networks to identify patients who risk the development of cytomegalovirus disease--a significant cause of mortality and morbidity in these patients. The neural networks we examined produced overall correct classifications well in excess of 80% in each of the two groups involved, diseased and non-diseased. These predictions are a considerable improvement on current methods and encourage the belief that renal transplantation data may respond well to analysis by neural networks.
Sheppard
D
D
Department of Computer Studies, University of Glamorgan, Pontypridd, Mid Glamorgan, UK.
McPhee
D
D
Darke
C
C
Shrethra
B
B
Moore
R
R
Jurewitz
A
A
Gray
A
A
D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D003586:Cytomegalovirus Infections / Q000175:diagnosis* / Q000473:pathology; D016208:Databases, Factual; D005260:Female; D005544:Forecasting; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016030:Kidney Transplantation; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011183:Postoperative Complications; D006435:Renal Dialysis; D051437:Renal Insufficiency / Q000628:therapy; D018570:Risk Assessment