Therapeutic Drug Monitoring of Kidney Transplant Recipients Using Profiled Support Vector Machines
G. Camps, E. Soria, J. Perez, Fernando Perez-Cruz, A. Artes-Rodriguez and N. V. Jimenez-Torres
IEEE Transactions on Systems, Man and Cybernetics, Part C Volume 37, Number 3, pp. 359-372, 2007.

Abstract

This work proposes a twofold approach for therapeutic drug monitoring (TDM) of kidney recipients using Support Vector Machines (SVM), for both predicting and detecting Cyclosporine A (CyA) blood concentrations. The final goal is to build useful, robust, and ultimately understandable models for individualising the dosage of CyA. We compare SVM with several neural network models, such as the multilayer perceptron (MLP), the Elman recurrent network, FIR/IIR networks, and Neural Network ARMAX approaches. In addition, we present a profile-dependent SVM (PD-SVM), which incorporates a priori knowledge in both tasks. Models are compared numerically, statistically, and in the presence of additive noise. Data from fifty-seven renal allograft recipients were used to develop the models. Patients followed a standard triple therapy and CyA trough concentration was the dependent variable. The best results for the CyA blood concentration prediction were obtained using the PD-SVM (mean error of 0.36 ng/mL and root-mean-square-error of 52.01 ng/mL in the validation set) and appeared to be more robust in the presence of additive noise. The propose PD-SVM improved results from the standard SVM and MLP, specially significant (both numerical and statistically) in the one-against-all scheme. Finally, some clinical conclusions were obtained from sensitivity rankings of the models and distribution of support vectors. We conclude that the PD-SVM approach produces more accurate and robust models than neural networks. Finally, a software tool for aiding medical

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