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

arXiv: https://arxiv.org/abs/1901.01816 · HTML: https://mlsanity.com/paper/1901.01816/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2019.

## Built on by (most cited first, 8 of 8)

- [Machine Learning based prediction of Glucose Levels in Type 1 Diabetes Patients with the use of Continuous Glucose Monitoring Data](https://mlsanity.com/paper/2302.12856/) (2023)
- [The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes](https://mlsanity.com/paper/2503.18592/) (2025)
- [LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management](https://mlsanity.com/paper/2103.06708/) (2021)
- [Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning](https://mlsanity.com/paper/2307.06501/) (2023)
- [Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation](https://mlsanity.com/paper/2310.14976/) (2023)
- [Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment](https://mlsanity.com/paper/2505.14477/) (2025)
- [Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning](https://mlsanity.com/paper/2505.21596/) (2025)
- [Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems](https://mlsanity.com/paper/2604.26126/) (2026)

## Abstract (arXiv)

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.