用强化学习个性化调整糖尿病患者胰岛素，显著提升血糖控制效果。
Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment
- 基于强化学习构建个性化胰岛素调节模型，自动优化基础与餐时剂量。
- 在模拟人群中，新方法使血糖在目标范围时间提升，低/高血糖事件减少。
- 适合需要精细血糖管理的1型和2型糖尿病患者，为临床试验提供依据。
尽管胰岛素制剂与技术不断进步，但对多数1型糖尿病（T1D）和长期2型糖尿病（T2D）患者而言，胰岛素调整仍是挑战。本研究提出自适应基础-追加顾问（ABBA），一种基于强化学习的个性化胰岛素治疗推荐方法，适用于进行自我血糖监测和每日多次注射的T1D与T2D患者。通过使用经美国食品药品监督管理局（FDA）认可的群体（包含101名模拟的T1D成人和101名T2D成人）进行体外评估，结果显示，相较于标准基础-追加顾问（BBA），ABBA显著提升了时间在目标范围（TIR）比例，并显著降低低于和高于目标范围的时间。ABBA性能在两个月内持续改善，而BBA仅表现出微小变化。该个性化胰岛素调整方法有望进一步优化血糖控制，支持患者日常自我管理，其结果支持首次开展人体试验。
原文摘要 · Abstract (English)
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.
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