Toward a Fully Automated Artificial Pancreas System Using a Bioinspired Reinforcement Learning Design: In Silico Validation
Toward a Fully Automated Artificial Pancreas System Using a Bioinspired Reinforcement Learning Design: In Silico Validation
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DOI：
10.1109/jbhi.2020.3002022
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发表时间：
2021-02-01
影响因子：
7.7
通讯作者：
Park, Sung-Min
中科院分区：
文献类型：
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作者：
Lee, Seunghyun;Kim, Jiwon;Park, Sung-Min
目的：由于意外的外源事件（例如膳食摄入），胰岛素治疗的自动化是 1 型糖尿病血糖管理中最具挑战性的方面。在本文中，我们为全自动人工胰腺（AP）系统提出了一种基于强化学习（RL）的新型人工智能（AI）算法。方法：开发了一种用于自动胰岛素输注的仿生 RL 设计方法。该方法具有暗示时间稳态目标的奖励函数和反映个体特定药理学特征的折扣因子。所提出的方法应用于使用 RL 算法的训练方法，并在来自 FDA 批准的 UVA/Padova 模拟器的虚拟患者中进行了评估，这些患者的膳食摄入量未经事先通知。结果：对于餐前禁食的单餐实验，经过训练的策略在基础阶段和餐后阶段都表现出完全自动化的调节。在具有不同胰岛素敏感性和黎明现象的计算机试验中，该策略的平均血糖达到 124.72 mgAL，正常范围内的时间百分比为 89.56%。分层相关性传播提供了有关人工智能驱动决策的可解释信息，以实现对传感器噪声的鲁棒性、自动餐后调节和避免胰岛素堆积。结论：基于仿生 RL 方法的 AP 算法可以实现全自动血糖控制，并且无需通知膳食摄入量。意义：所提出的框架可以扩展到具有重大不确定性的系统的其他基于药物的治疗。
Objective: The automation of insulin treatment is the most challenge aspect of glucose management for type 1 diabetes owing to unexpected exogenous events (e.g., meal intake). In this article, we propose a novel reinforcement learning (RL) based artificial intelligence (AI) algorithm for a fully automated artificial pancreas (AP) system. Methods: A bioinspired RL designing method was developed for automated insulin infusion. This method has reward functions that imply the temporal homeostatic objective and discount factors that reflect an individual specific pharmacological characteristic. The proposed method was applied to a training method using an RL algorithm and was evaluated in virtual patients from the FDA approved UVA/Padova simulator with unannounced meal intakes. Results: For a single-meal experiment with preprandial fasting, the trained policy demonstrated fully automated regulation in both the basal and postprandial phases. In the in silico trial with a variation of insulin sensitivity and dawn phenomenon, the policy achieved a mean glucose of 124.72 mgAL and percentage time in the normal range of 89.56%. The layer-wise relevance propagation provides interpretable information on AI-driven decision for robustness to sensor noise, automated postprandial regulation, and insulin stacking avoidance. Conclusion: The AP algorithm based on the bioinspired RL approach enables fully automated blood glucose control with unannounced meal intake. Significance: The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties.