A genetic algorithm-based, hybrid machine learning approach to model selection.
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.
Bies
Robert R
RR
Department of Pharmaceutical Sciences and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. rrb47@pitt.edu
Muldoon
Matthew F
MF
Pollock
Bruce G
BG
Manuck
Steven
S
Smith
Gwenn
G
Sale
Mark E
ME
D000465:Algorithms; D018687:Antidepressive Agents, Second-Generation / Q000008:administration & dosage / Q000493:pharmacokinetics / Q000494:pharmacology; D001185:Artificial Intelligence; D015283:Citalopram / Q000008:administration & dosage / Q000493:pharmacokinetics / Q000494:pharmacology; D003198:Computer Simulation; D006801:Humans; D007262:Infusions, Intravenous; D008954:Models, Biological; D008957:Models, Genetic; D012984:Software
D018687:Antidepressive Agents, Second-Generation; D015283:Citalopram