Therapeutic drug dosing prediction using adaptive models and artificial neural networks

Using process control approaches, pharmacokinetic models are developed. All models presented are based on the one-compartment model. These neural network-based and adaptive versions of linear, nonlinear, and time-dependent models are tested on data sets collected from hemodialysis patients receiving anti-coagulant heparin during treatment. Results show that increased model complexity ensures improved quality of identification, while it decreases the initial quality of estimation