Fuzzy-Logic-Based Supervisor of Insulin Bolus Delivery for Patients with Type 1 Diabetes Mellitus

Abstract

In this article, a fuzzy-logic-based supervisor of insulin bolus delivery for type 1 diabetes mellitus (T1DM) is proposed. The proposed supervisor incorporates expert knowledge into three phases, including recall, inference, and learning phases. A recently developed and well-acknowledged meal simulation model of the glucose–insulin system for T1DM was employed to create virtual subjects for testing. Data from virtual subjects were used to identify an intermediate physiological model, and then our proposed supervisor was synthesized based on this intermediate model. The key features of this fuzzy-logic-based supervisor are that the implementation does not need an online model and it can gradually be updated meal-by-meal. In addition, only two blood glucose measurements between each meal are needed for updating the insulin bolus delivery. The simulation results show that effective and robust glycemic control performance can be achieved. This methodology can be widely applied to patients with continuous subcutaneous insulin infusion (CSII) or multiple daily injections (MDI)