Journal
JOURNAL OF INTERNATIONAL MEDICAL RESEARCH
Volume 37, Issue 6, Pages 1680-1691Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/147323000903700603
Keywords
ARTIFICIAL INTELLIGENCE; NEURAL NETWORKS (COMPUTER); PHARMACOKINETICS; REMIFENTANIL
Funding
- 2006 Inje University Special Research Grant
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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 (R); 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.
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