# Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment

arXiv: https://arxiv.org/abs/2505.14477 · HTML: https://mlsanity.com/paper/2505.14477/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Reinforcement Learning for Closed-Loop Blood Glucose Control](https://mlsanity.com/paper/2009.09051/) (2020)
- [Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes](https://mlsanity.com/paper/2204.03376/) (2022)
- [Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation](https://mlsanity.com/paper/2005.09059/) (2020)
- [A dual mode adaptive basal-bolus advisor based on reinforcement learning](https://mlsanity.com/paper/1901.01816/) (2019)
- [A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.04059/) (2019)
- [Food Recognition and Nutritional Apps](https://mlsanity.com/paper/2307.05372/) (2023)
- [Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments](https://mlsanity.com/paper/2309.09125/) (2023)
- [The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes](https://mlsanity.com/paper/2503.18592/) (2025)

## Abstract (arXiv)

Despite recent advances in insulin preparations and technology, adjusting insulin remains an ongoing challenge for the majority of people with type 1 diabetes (T1D) and longstanding type 2 diabetes (T2D). In this study, we propose the Adaptive Basal-Bolus Advisor (ABBA), a personalised insulin treatment recommendation approach based on reinforcement learning for individuals with T1D and T2D, performing self-monitoring blood glucose measurements and multiple daily insulin injection therapy. We developed and evaluated the ability of ABBA to achieve better time-in-range (TIR) for individuals with T1D and T2D, compared to a standard basal-bolus advisor (BBA). The in-silico test was performed using an FDA-accepted population, including 101 simulated adults with T1D and 101 with T2D. An in-silico evaluation shows that ABBA significantly improved TIR and significantly reduced both times below- and above-range, compared to BBA. ABBA's performance continued to improve over two months, whereas BBA exhibited only modest changes. This personalised method for adjusting insulin has the potential to further optimise glycaemic control and support people with T1D and T2D in their daily self-management. Our results warrant ABBA to be trialed for the first time in humans.