A dual mode adaptive basal-bolus advisor based on reinforcement learning

TL;DR

This paper introduces an adaptive insulin dosing algorithm for type 1 diabetes patients that uses reinforcement learning to personalize basal-bolus insulin recommendations based on either SMBG or CGM data, validated through in silico simulations.

Contribution

The paper presents a novel reinforcement learning-based adaptive algorithm that personalizes insulin dosing using different glucose monitoring inputs, demonstrating comparable performance with both SMBG and CGM.

Findings

Achieves similar glucose control with SMBG and CGM inputs.

Maintains total daily insulin dose while adapting to patient variability.

Validated in silico with realistic scenarios over three months.

Abstract

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.