Example-based Support Vector Machine for Drug Concentration Analysis

Machine learning has been largely applied to ana- lyze 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 (SVM) 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.


Published in:
Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2011), 153-157
Presented at:
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2011), Boston, Massachusetts, USA, August 30 - September 3, 2011
Year:
2011
Publisher:
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa
ISBN:
978-1-4244-4122-8
Keywords:
Laboratories:




 Record created 2011-10-06, last modified 2018-09-13

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