The development of closed-loop systems for glycemia control in type I diabetes relies heavily on simulated patients. Improving the performances and adaptability of these close-loops raises the risk of over-fitting the simulator. This may have dire consequences, especially in unusual cases which were not faithfully-if at all-captured by the simulator. To address this, we propose to use offline RL agents, trained on real patient data, to perform the glycemia control. To further improve the performances, we propose an end-to-end personalization pipeline, which leverages offline-policy evaluation methods to remove altogether the need of a simulator, while still enabling an estimation of clinically relevant metrics for diabetes.

翻译：1型糖尿病血糖控制闭环系统的开发严重依赖于模拟患者。提升这些闭环系统的性能与适应性会增加对模拟器过拟合的风险。这可能导致严重后果，尤其是在模拟器未能忠实地（甚至完全未能）捕捉到的异常病例中。为解决此问题，我们提出使用基于真实患者数据训练的离线强化学习智能体进行血糖控制。为进一步提升性能，我们提出了一种端到端个性化管线，该管线利用离线策略评估方法完全消除对模拟器的依赖，同时仍能实现对糖尿病临床相关指标的估计。