A personalized approach to insulin regulation using brain-inspired neural sematic memory in diabetic glucose control

Diabetes mellitus is a chronic disease with a high incidence rate worldwide. In Type-1 diabetes, the failure to produce sufficient pancreatic insulin leads to an uncontrolled increase in blood glucose. Prolong elevated blood glucose level poses significant risks of acute and chronic medical complications. Human assisted insulin injection, either through a fixed regime under the close supervision of a physician or through compartmental model schedules, is fundamentally an open-loop control system. Currently, a large amount of research has been conducted to treat Type-1 diabetes using a closed-loop insulin delivery system. The objective of this work is to investigate the use of a brain-inspired neural fuzzy system as a controller to deliver insulin in a closed-loop system for the treatment of Type-1 diabetes. In this paper, the Pseudo-Outer Product based Fuzzy Neural Network using the Yager rule of inference (i.e. POP-Yager) is employed as an intelligent controller to dispense the appropriate amount of insulin in the presence of varying meal disturbances to achieve normoglycemia for a simulated Type-1 diabetic patient.