Nonparametric identification of pharmacokinetic population models via Gaussian processes
| Authors: | De Nicolao Giuseppe, Universita' di Pavia, Italy
Neve Marta, Universita' di Pavia, Italy
Marchesi Laura, Universita' di Pavia, Italy |
|---|
| Topic: | 8.2 Modelling & Control of Biomedical Systems |
|---|
| Session: | Identification of Biomedical System Dynamics |
|---|
| Keywords: | nonparametric identification, estimation theory, pharmacokinetic data, splines, neural networks, regularization |
|---|
Abstract
Population models are used to describe the behaviour of differentsubjects belonging to a population and play an important role in drug pharmacokinetics. A nonparametric identification scheme is proposed in which both the average population response and the individual ones are modelled as Gaussian stochastic processes. Assuming that the average curve is an integrated Wiener process, it is shown that its estimate is a cubic spline. An Empirical Bayes (EB) algorithm for estimating both the typical and the individual curves is worked out. The model is tested on xenobiotics pharmacokinetic data.
