Finite impulse response neural networks with applications in time series prediction
Finite impulse response neural networks with applications in time series prediction
Optimal linear combinations of neural networks
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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This paper proposes the use of neural networks ensemble for predicting the cyclosporine A (CyA)concen tration 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)w ere 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)sho wed the best results. A committee of trained networks improved accuracy (RMSE = 44.77 ng/mL in validation).