Neural Networks Ensemble for Cyclosporine Concentration Monitoring

Abstract

This paper proposes the use of neural networks ensemble for predicting the cyclosporine A (CyA) concentration in kidney transplant patients. In order to optimize clinical outcomes and to reduce the cost associated with patient care, accurate prediction of CyA concentrations is the main objective of therapeutic drug monitoring.

Thirty-two renal allograft patients and different factors (age, weight, gender, creatinine and post-transplantation days, together with past dosages and concentrations) were studied to obtain the best models. Three kinds of networks (multilayer perceptron, FIR network, Elman recurrent network) and the formation of neural-network ensembles were used. The FIR network, yielding root-mean-squared errors (RMSE) of 41.61 ng/mL in training (22 patients) and 52.34 ng/mL in validation (10 patients) showed the best results. A committee of trained networks improved accuracy (RMSE = 44.77 ng/mL in validation).

This paper has been partially supported by the European FEDER Project IFD1997-0935 entitled ”Desarrollo de Sistemas Neuronales Aplicados en Atención Farmacéutica”.