Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes

The closed loop control of blood glucose levels might help to reduce many short- and long-term complications of type 1 diabetes. Continuous glucose monitoring and insulin pump systems have facilitated the development of the artificial pancreas. In this paper, artificial neural networks are used for both the identification of patient dynamics and the glycaemic regulation. A subcutaneous glucose measuring system together with a Lispro insulin subcutaneous pump were used to gather clinical data for each patient undergoing treatment, and a corresponding in silico and ad hoc neural network model was derived for each patient to represent their particular glucose–insulin relationship. Based on this nonlinear neural network model, an ad hoc neural network controller was designed to close the feedback loop for glycaemic regulation of the in silico patient. Both the neural network model and the controller were tested for each patient under simulation, and the results obtained show a good performance during food intake and variable exercise conditions.

► We use artificial neural networks for the identification of patient dynamics with type 1 diabetes. ► We design a neural network controller to close the feedback loop for glycaemic regulation. ► Both the neural network model and the controller are tested for each patient under simulation. ► The results obtained show a good performance during food intake and variable exercise conditions. ► Neither a glucose–insulin mathematical model nor a standard controller structure are required.