title: Example-based support vector machine for drug concentration analysis.
abstract: 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.
authors.affiliation: Integrated Systems Laboratory, EPFL, Switzerland.
authors.forename: Wenqi
authors.initials: W
authors.lastname: You
authors.forename: Nicolas
authors.initials: N
authors.lastname: Widmer
authors.forename: Giovanni
authors.initials: G
authors.lastname: De Micheli
mesh_terms: D000465:Algorithms; D004305:Dose-Response Relationship, Drug; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D057285:Precision Medicine / Q000379:methods*; D060388:Support Vector Machine