Inproceedings: An article in a conference proceedings.
Example-based support vector machine for drug concentration analysis
Title of the conference
EMBC 2011, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Boston, Massachusetts, United-States, August 30-September 3, 2011
Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2011)
Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.
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