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 |
|---|---|
| Pages (from-to) | 195-221 |
| Number of pages | 27 |
| Journal | Journal of Pharmacokinetics and Pharmacodynamics |
| Volume | 33 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 2006 |
Keywords
- Automated machine learning
- Covariate selection
- Genetic algorithm
- Model building
- Nonlinear mixed effects modeling
- Population paramacokinetics
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