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.
Department of Pharmaceutical Sciences and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. rrb47@pitt.edu
Robert R
RR
Bies
Matthew F
MF
Muldoon
Bruce G
BG
Pollock
Steven
S
Manuck
Gwenn
G
Smith
Mark E
ME
Sale
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