Abstract
We describe a general and robust method for identification of an optimal non-linear mixed effects model. This includes structural, inter-individual random effects, covariate effects and residual error models using machine learning. This method is based on combinatorial optimization using genetic algorithm.
Original language | English (US) |
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Pages (from-to) | 195-221 |
Number of pages | 27 |
Journal | Journal of Pharmacokinetics and Pharmacodynamics |
Volume | 33 |
Issue number | 2 |
DOIs | |
State | Published - Apr 1 2006 |
Externally published | Yes |
Keywords
- Automated machine learning
- Covariate selection
- Genetic algorithm
- Model building
- Nonlinear mixed effects modeling
- Population paramacokinetics
ASJC Scopus subject areas
- Pharmacology