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Neural and Kernal Methods for Therapeutic Drug Monitoring

Neural and Kernal Methods for Therapeutic Drug Monitoring

G. Camps-Valls, J. D. Martin-Guerrero
Copyright: © 2006 |Pages: 24
ISBN13: 9781591408482|ISBN10: 1591408482|EISBN13: 9781591408505
DOI: 10.4018/978-1-59140-848-2.ch011
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MLA

Camps-Valls, G., and J. D. Martin-Guerrero. "Neural and Kernal Methods for Therapeutic Drug Monitoring." Neural Networks in Healthcare: Potential and Challenges, edited by Rezaul Begg, et al., IGI Global, 2006, pp. 238-261. https://doi.org/10.4018/978-1-59140-848-2.ch011

APA

Camps-Valls, G. & Martin-Guerrero, J. D. (2006). Neural and Kernal Methods for Therapeutic Drug Monitoring. In R. Begg, J. Kamruzzaman, & R. Sarker (Eds.), Neural Networks in Healthcare: Potential and Challenges (pp. 238-261). IGI Global. https://doi.org/10.4018/978-1-59140-848-2.ch011

Chicago

Camps-Valls, G., and J. D. Martin-Guerrero. "Neural and Kernal Methods for Therapeutic Drug Monitoring." In Neural Networks in Healthcare: Potential and Challenges, edited by Rezaul Begg, Joarder Kamruzzaman, and Ruhul Sarker, 238-261. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-848-2.ch011

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Abstract

Recently, important advances in dosage formulations, therapeutic drug monitoring (TDM), and the emerging role of combined therapies have resulted in a substantial improvement in patients’ quality of life. Nevertheless, the increasing amounts of collected data and the non-linear nature of the underlying pharmacokinetic processes justify the development of mathematical models capable of predicting concentrations of a given administered drug and then adjusting the optimal dosage. Physical models of drug absorption and distribution and Bayesian forecasting have been used to predict blood concentrations, but their performance is not optimal and has given rise to the appearance of neural and kernel methods that could improve it. In this chapter, we present a complete review of neural and kernel models for TDM. All presented methods are theoretically motivated, and illustrative examples in real clinical problems are included.

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