Introduction to artificial neural network by zurada pdf printer

#INTRODUCTION TO ARTIFICIAL NEURAL NETWORK BY ZURADA PDF PRINTER MANUAL#

Population pharmacokinetics of gentamicin in neonates. Estimation of gentamicin clearance and volume of distribution in neonates and young children. Measuring the predictive performance of computer-controlled infusion pumps. Some suggestions for measuring predictive performance. Sheiner (Eds) NONMEM Project Group, University of California at San Francisco, San Francisco, 1992. International Joint Conference on Neural Networks. Acceleration of back-propagation through learning rate and momentum adaptation. Increased rates of convergence through learning rate adaptation.

#INTRODUCTION TO ARTIFICIAL NEURAL NETWORK BY ZURADA PDF PRINTER MANUAL#

Pocket Manual of drug use in clinical medicine 3rd Edition. Application of neural networks to pharmacodynamics. St Paul MN: West Publishing Company, 1992. Introduction to artificial neural systems. Neural networks may be useful in predicting the clinical pharmacokinetics of drugs. It is concluded that neural networks can predict serum drug concentrations of gentamicin.

The observed peak serum concentration distribution was multimodal and the neural network reproduced this distribution with less difference between the actual distribution and the predicted distribution than NONMEM. NONMEM provided numerically more precise and less biased predictions when extrapolating outside the 2.5 and 6.0 Вµg/ml range. Average prediction errors for serum trough concentrations were 48.3% for neural networks and 59.0% for NONMEM. The prediction error for peak serum concentrations averaged 16.5% for the neural networks and 18.6% for NONMEM. Trough serum concentration predictions were similar using both neural networks and NONMEM. Neural networks made peak serum concentration predictions within the 2.5-6.0 Вµg/ml range with statistically less bias and comparable precision with paired NONMEM predictions. Predictions were also made on 33 observations that were outside the 2.5 and 6.0 Вµg/ml range. Predictions were made in 111 patients with peak concentrations between 2.5 and 6.0 Вµg/ml using the patient factors age, height, weight, dose, dose interval, body surface area, serum creatinine, and creatinine clearance. Predictions of steady state peak and trough serum gentamicin concentrations were compared between a traditional population kinetic method using the computer program NONMEM to an empirical approach using neural networks.

Introduction to artificial neural network by zurada pdf printer