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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.14477 |
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| _version_ | 1866916910406303744 |
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| author | Panagiotou, Maria Brigato, Lorenzo Streit, Vivien Hayoz, Amanda Proennecke, Stephan Athanasopoulos, Stavros Olsen, Mikkel T. Brok, Elizabeth J. den Svensson, Cecilie H. Makrilakis, Konstantinos Xatzipsalti, Maria Vazeou, Andriani Mertens, Peter R. Pedersen-Bjergaard, Ulrik de Galan, Bastiaan E. Mougiakakou, Stavroula |
| author_facet | Panagiotou, Maria Brigato, Lorenzo Streit, Vivien Hayoz, Amanda Proennecke, Stephan Athanasopoulos, Stavros Olsen, Mikkel T. Brok, Elizabeth J. den Svensson, Cecilie H. Makrilakis, Konstantinos Xatzipsalti, Maria Vazeou, Andriani Mertens, Peter R. Pedersen-Bjergaard, Ulrik de Galan, Bastiaan E. Mougiakakou, Stavroula |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment Panagiotou, Maria Brigato, Lorenzo Streit, Vivien Hayoz, Amanda Proennecke, Stephan Athanasopoulos, Stavros Olsen, Mikkel T. Brok, Elizabeth J. den Svensson, Cecilie H. Makrilakis, Konstantinos Xatzipsalti, Maria Vazeou, Andriani Mertens, Peter R. Pedersen-Bjergaard, Ulrik de Galan, Bastiaan E. Mougiakakou, Stavroula Machine Learning 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. |
| title | Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.14477 |