People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.

中文翻译：

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使用深度强化学习控制 1 型糖尿病的基础血糖：计算机验证


1 型糖尿病 (T1D) 患者需要定期外源性输注胰岛素，以将血糖浓度维持在治疗所需的目标范围内。尽管人工胰腺和连续血糖监测已被证明可以有效实现闭环控制，但由于血糖动态的高度复杂性和技术的局限性，仍然存在重大挑战。在这项工作中，我们提出了一种用于单激素（胰岛素）和双激素（胰岛素和胰高血糖素）输送的新型深度强化学习模型。特别是，传递策略是通过双 Q 学习和扩张循环神经网络开发的。为了设计和测试目的，采用了 FDA 认可的 UVA/Padova 1 型模拟器。首先，我们进行长期广义训练以获得群体模型。然后，使用特定于主题的数据的小数据集对该模型进行个性化。计算机结果表明，与使用低葡萄糖胰岛素悬浮液的标准基础推注疗法相比，单激素和双激素递送策略实现了良好的血糖控制。具体而言，在成人队列 (n = 10) 中，单激素控制下目标范围 70、180 mg/dL 内的时间百分比从 77.6% 改善至 80.9%，而双激素控制下则从 85.6% 改善。在青少年队列 (n = 10) 中，单激素控制下目标范围内的时间百分比从 55.5% 提高到 65.9%，双激素控制下则从 78.8% 提高到 78.8%。在所有情况下，都观察到低血糖发生率显着降低。这些结果表明，使用深度强化学习是 T1D 闭环血糖控制的可行方法。