Poynton M R, Choi B M, Kim Y M, Park I S, Noh G J, Hong S O, Boo Y K, Kang S H
Informatics Program, University of Utah College of Nursing and Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
J Int Med Res. 2009 Nov-Dec;37(6):1680-91. doi: 10.1177/147323000903700603.
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.
