A dual mode adaptive basal-bolus advisor based on reinforcement learning
This addresses personalized insulin optimization for type 1 diabetes patients, but it is incremental as it adapts existing RL methods to a specific medical application.
The paper tackled personalized insulin dosing for type 1 diabetes patients by developing an adaptive basal-bolus algorithm (ABBA) using reinforcement learning, which achieved comparable glucose control performance with either SMBG or CGM inputs without altering total daily insulin dose in a 100-adult simulation over three months.
Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients' glucose level on the previous day. The ABBA is based on reinforcement learning (RL), a type of artificial intelligence, and was validated in silico with an FDA-accepted population of 100 adults under different realistic scenarios lasting three simulated months. The scenarios involve three main meals and one bedtime snack per day, along with different variabilities and uncertainties for insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement time. The results indicate that the proposed approach achieves comparable performance with CGM or SMBG as input signals, without influencing the total daily insulin dose. The results are a promising indication that AI algorithmic approaches can provide personalised adaptive insulin optimisation and achieve glucose control - independently of the type of glucose monitoring technology.