The self-aware diabetic patient software agent model

This work presents a self-aware diabetic patient software agent for representing a human diabetic patient. To develop a 24 h, stochastic and self-aware patient agent, we extend the original seminal work of Ackerman et al. [1] in creating a mathematical model of human blood glucose levels in three aspects. (1) We incorporate the stochastic and unpredictable effects of daily living. (2) The Ackerman model is extended into the period of night-time. (3) Patients' awareness of their own conditions is incorporated. Simulation results are quantitatively assessed to demonstrate the effectiveness of lifestyle management, such as adjusting the amount of food consumed, meal schedule, intensity of exercise and level of medication. In this work we show through the simulation that the average blood glucose can be reduced by as much as 51% due to careful lifestyle management. Self monitoring blood glucose is also quantitatively evaluated. The simulation results show that the average blood glucose is further dropped by 25% with the assistance of blood glucose samples. In addition, the blood glucose is perfectly controlled in the target range during the simulation period as a result of joint efforts of lifestyle management and self monitoring blood glucose. This study focuses on demonstrating how human patients' behavior, specifically lifestyle and self monitoring of blood glucose, affects blood glucose controls on a daily basis. This work does not focus on the insulin-glucose interaction of an individual human patient. Our conclusion is that this self-aware patient agent model is capable of adequately representing diabetic patients and of evaluating their dynamic behaviors. It can also be incorporated into a multi-agent system by introducing other healthcare components so that more interesting insights such as the healthcare quality, cost and performance can be observed.