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.
Poynton
M R
MR
Informatics Program, University of Utah College of Nursing and Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Choi
B M
BM
Kim
Y M
YM
Park
I S
IS
Noh
G J
GJ
Hong
S O
SO
Boo
Y K
YK
Kang
S H
SH
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