Parameterized SVM for personalized drug concentration prediction

• Published in: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2013; pp. 5789 - 5792
• Main Authors: Wenqi You, Simalatsar, Alena, De Micheli, Giovanni
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2013
• Subjects: Algorithms; Antineoplastic Agents - metabolism; Antineoplastic Agents - therapeutic use; Benzamides - metabolism; Benzamides - therapeutic use; Discrete cosine transforms; Drug Monitoring; Drugs; Humans; Imatinib Mesylate; Leukemia, Myelogenous, Chronic, BCR-ABL Positive - drug therapy; Libraries; Pharmaceutical Preparations - metabolism; Piperazines - metabolism; Piperazines - therapeutic use; Precision Medicine; Prediction algorithms; Pyrimidines - metabolism; Pyrimidines - therapeutic use; Support Vector Machine; Support vector machines; Time division multiplexing; Time Factors; Time measurement
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2013.6610867

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.