Parameterized SVM for personalized drug concentration prediction
This paper proposes a parameterized Support Vector Machine (ParaSVM) approach for modeling the Drug Concentration to Time (DCT) curves. It combines the merits of Support Vector Machine (SVM) algorithm that considers various patient features and an analytical model that approximates the predicted DCT...
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Published in | 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2013; pp. 5789 - 5792 |
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Main Authors | , , |
Format | Conference Proceeding Journal Article |
Language | English |
Published |
United States
IEEE
01.01.2013
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Subjects | |
Online Access | Get full text |
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Summary: | This paper proposes a parameterized Support Vector Machine (ParaSVM) approach for modeling the Drug Concentration to Time (DCT) curves. It combines the merits of Support Vector Machine (SVM) algorithm that considers various patient features and an analytical model that approximates the predicted DCT points and enables curve calibrations using occasional real Therapeutic Drug Monitoring (TDM) measurements. The RANSAC algorithm is applied to construct the parameter library for the relevant basis functions. We show an example of using ParaSVM to build DCT curves and then calibrate them by TDM measurements on imatinib case study. |
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ISSN: | 1094-687X 1557-170X 1558-4615 |
DOI: | 10.1109/EMBC.2013.6610867 |