Nonparametric identification of population models via Gaussian processes
Population models are used to describe the dynamics of different subjects belonging to a population and play an important role in drug pharmacokinetics. A nonparametric identification scheme is proposed in which both the average impulse response of the population and the individual ones are modelled...
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| Published in | Automatica (Oxford) Vol. 43; no. 7; pp. 1134 - 1144 |
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| Main Authors | , , |
| Format | Journal Article Conference Proceeding |
| Language | English |
| Published |
Oxford
Elsevier Ltd
01.07.2007
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0005-1098 1873-2836 |
| DOI | 10.1016/j.automatica.2006.12.024 |
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| Abstract | Population models are used to describe the dynamics of different subjects belonging to a population and play an important role in drug pharmacokinetics. A nonparametric identification scheme is proposed in which both the average impulse response of the population 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 algorithm for estimating both the average and the individual curves is worked out. The model is tested on simulated data sets as well as on xenobiotics pharmacokinetic data. |
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| AbstractList | Population models are used to describe the dynamics of different subjects belonging to a population and play an important role in drug pharmacokinetics. A nonparametric identification scheme is proposed in which both the average impulse response of the population 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 algorithm for estimating both the average and the individual curves is worked out. The model is tested on simulated data sets as well as on xenobiotics pharmacokinetic data. |
| Author | Marchesi, L. De Nicolao, G. Neve, M. |
| Author_xml | – sequence: 1 givenname: M. surname: Neve fullname: Neve, M. email: marta.neve@unipv.it – sequence: 2 givenname: G. surname: De Nicolao fullname: De Nicolao, G. email: giuseppe.denicolao@unipv.it – sequence: 3 givenname: L. surname: Marchesi fullname: Marchesi, L. |
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| Keywords | Splines Neural networks Regularization Nonparametric identification Estimation theory Pharmacokinetic data Drug Non parametric estimation Wiener process Neural network Stochastic process Modeling Spline approximation Cubic spline Gaussian process Model matching Pulse response Population dynamics System identification Pharmacokinetics |
| Language | English |
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| SubjectTerms | Applied sciences Biological and medical sciences Computer science; control theory; systems Control theory. Systems Estimation theory Exact sciences and technology General pharmacology Medical sciences Modelling and identification Neural networks Nonparametric identification Pharmacokinetic data Pharmacokinetics. Pharmacogenetics. Drug-receptor interactions Pharmacology. Drug treatments Regularization Splines |
| Title | Nonparametric identification of population models via Gaussian processes |
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