Prediction of cyclosporine dosage in patients after kidney transplantation using neural networks
| Autor: | Gustavo, Camps-Valls, Begoña, Porta-Oltra, Emilio, Soria-Olivas, José David, Martín-Guerrero, Antonio José, Serrano-López, Juan José, Pérez-Ruixo, Nicolás Víctor, Jiménez-Torres |
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| Rok vydání: | 2003 |
| Předmět: |
Graft Rejection
Mean squared error Finite impulse response Remote patient monitoring Statistics as Topic Biomedical Engineering Administration Oral computer.software_genre Models Biological Drug Administration Schedule Predictive Value of Tests Robustness (computer science) Humans Medicine Kidney transplantation Artificial neural network medicine.diagnostic_test business.industry Models Cardiovascular Pattern recognition Mycophenolic Acid medicine.disease Kidney Transplantation Drug Therapy Computer-Assisted Therapeutic drug monitoring Multilayer perceptron Cyclosporine Prednisone Drug Therapy Combination Neural Networks Computer Data mining Artificial intelligence business computer Algorithms |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 50:442-448 |
| ISSN: | 1558-2531 0018-9294 |
| DOI: | 10.1109/tbme.2003.809498 |
| Popis: | This paper proposes the use of neural networks for individualizing the dosage of cyclosporine A (CyA) in patients who have undergone kidney transplantation. Since the dosing of CyA usually requires intensive therapeutic drug monitoring, the accurate prediction of CyA blood concentrations would decrease the monitoring frequency and, thus, improve clinical outcomes. Thirty-two patients and different factors were studied to obtain the models. Three kinds of networks (multilayer perceptron, finite impulse response (FIR) network, and Elman recurrent network) and the formation of neural-network ensembles are used in a scheme of two chained models where the blood concentration predicted by the first model constitutes an input to the dosage prediction model. This approach is designed to aid in the process of clinical decision making. The FIR network, yielding root-mean-square errors (RMSEs) of 52.80 ng/mL and mean errors (MEs) of 0.18 ng/mL in validation (10 patients) showed the best blood concentration predictions and a committee of trained networks improved the results (RMSE = 46.97 ng/mL, ME = 0.091 ng/mL). The Elman network was the selected model for dosage prediction (RMSE = 0.27 mg/Kg/d, ME = 0.07 mg/Kg/d). However, in both cases, no statistical differences on the accuracy of neural methods were found. The models' robustness is also analyzed by evaluating their performance when noise is introduced at input nodes, and it results in a helpful test for models' selection. We conclude that neural networks can be used to predict both dose and blood concentrations of cyclosporine in steady-state. This novel approach has produced accurate and validated models to be used as decision-aid tools. |
| Databáze: | OpenAIRE |
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