Machine learning methods applied to pharmacokinetic modelling of remifentanil in healthy volunteers: a multi-method comparison.
This study compared the blood concentrations of remifentanil obtained in a previous clinical investigation with the predicted remifentanil concentrations produced by different pharmacokinetic models: a non-linear mixed effects model created by the software NONMEM; an artificial neural network (ANN) model; a support vector machine (SVM) model; and multi-method ensembles. The ensemble created from the mean of the ANN and the non-linear mixed effects model predictions achieved the smallest error and the highest correlation coefficient. The SVM model produced the highest error and the lowest correlation coefficient. Paired t-tests indicated that there was insufficient evidence that the predicted values of the ANN, SVM and two multi-method ensembles differed from the actual measured values at alpha = 0.05. The ensemble method combining the ANN and non-linear mixed effects model predictions outperformed either method alone. These results indicated a potential advantage of ensembles in improving the accuracy and reducing the variance of pharmacokinetic models.
Informatics Program, University of Utah College of Nursing and Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
M R
MR
Poynton
B M
BM
Choi
Y M
YM
Kim
I S
IS
Park
G J
GJ
Noh
S O
SO
Hong
Y K
YK
Boo
S H
SH
Kang
D000293:Adolescent; D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D005260:Female; D006262:Health; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010880:Piperidines / Q000097:blood / Q000493:pharmacokinetics*; D016032:Randomized Controlled Trials as Topic; D000077208:Remifentanil; D012189:Retrospective Studies; D013997:Time Factors; D055815:Young Adult
D010880:Piperidines; D000077208:Remifentanil