Personalized Modeling for Drug Concentration Prediction Using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients’ features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features’ influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Published in:
Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI), 1523-1527
Presented at:
4th International Conference on Biomedical Engineering and Informatics (BMEI), Shanghai, China, October 15-17, 2011

 Record created 2011-11-22, last modified 2018-01-28

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