Abstract
Diabetic Type-1 patients have no pancreatic insulin secretion, and an insulin therapy is prescribed for them to regulate glucose absorption. There are several self monitoring devices for glucose, but not for insulin, which must be known and kept within certain limits to avoid damages to the body. Currently, it is possible to obtain real-time glucose measurements, so control schemes can be designed to control the glucose level using the insulin rate injected to the patient. In this work we present a black-box modeling of the insulin dynamics in different in silico patients using a recurrent neural network that could be used for on-line monitoring of the insulin concentration for a better treatment. The inputs for the identification is the rate of insulin (μU/dl/min) applied to the patient, and blood glucose concentration. The output is insulin concentration (μU/ml) present in the blood stream. The model is validated through numerical simulations.
This work is partly supported by UNAM-PAPIIT IN120009. The work of Marcos A. González Olvera and Ana G. Gallardo is supported by CONACyT.
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González-Olvera, M.A., Gallardo-Hernández, A.G., Tang, Y., Revilla-Monsalve, M.C., Islas-Andrade, S. (2009). A Continuous-Time Recurrent Neurofuzzy Network for Black-Box Modeling of Insulin Dynamics in Diabetic Type-1 Patients. In: Yu, W., Sanchez, E.N. (eds) Advances in Computational Intelligence. Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03156-4_22
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