A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning

By Qingnan Sun, Marko V. Jankovic, João Budzinski, Brett L. Moore, Peter Diem, C. Stettler, Stavroula G. Mougiakakou. 2019

68 citations recorded by the source corpus.

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, 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 optimization and achieve glucose control—independent of the type of glucose monitoring technology.

Subfields: Cardiology and Cardiovascular Medicine, Endocrinology, Diabetes and Metabolism, Molecular Biology

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1. Parisa Rashidi
2. Jenna Wiens
3. Patrick James Tighe
4. Azra Bihorac

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, neutral): “sensitivity variation [119], [120].”
• Deep Reinforcement Learning for Closed-Loop Blood Glucose Control (2020, background): “Many of these works investigated the use of RL to adapt existing insulin treatment regimens to learn a human-in-the-loop policy (Ngo et al., 2018; Oroojeni Mohammad Javad et al., 2015; Sun et al., 2018).”
• Artificial Intelligence and Surgical Decision-Making (2019, methodology): “46 The algorithm performed as well as standard intermittent self-monitoring and continuous glucose monitoring methods, but with fewer episodes of hypoglycemia.”