期刊
JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS
卷 33, 期 2, 页码 195-221出版社
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10928-006-9004-6
关键词
nonlinear mixed effects modeling; covariate selection; automated machine learning; genetic algorithm; population paramacokinetics; model building
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.
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