论文标题
MPC引导的模仿神经网络政策的人造胰腺
MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas
论文作者
论文摘要
尽管模型预测控制（MPC）当前是人工胰腺（AP）中胰岛素控制的主要算法，但它通常需要复杂的在线优化，这对于资源受限的医疗设备来说是不可行的。 MPC通常还依赖于状态估计，这是一个容易出错的过程。在本文中，我们介绍了一种新型的AP控制方法，该方法使用模仿学习从MPC计算的演示中综合了神经网络胰岛素政策。此类政策在计算上是有效的，并且通过在培训时间使用完整的状态信息来仪器MPC，它们可以直接将测量结果映射到最佳治疗决策中，从而绕开状态估计。我们通过Monte Carlo辍学应用贝叶斯推断来学习政策，这使我们能够量化预测不确定性，从而得出更安全的治疗决定。我们表明，我们在特定患者模型下接受训练的控制策略很容易概括为患者队列的模型参数和干扰分布），并以状态估计的方式始终超过传统的MPC。
Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices. MPC also typically relies on state estimation, an error-prone process. In this paper, we introduce a novel approach to AP control that uses Imitation Learning to synthesize neural-network insulin policies from MPC-computed demonstrations. Such policies are computationally efficient and, by instrumenting MPC at training time with full state information, they can directly map measurements into optimal therapy decisions, thus bypassing state estimation. We apply Bayesian inference via Monte Carlo Dropout to learn policies, which allows us to quantify prediction uncertainty and thereby derive safer therapy decisions. We show that our control policies trained under a specific patient model readily generalize (in terms of model parameters and disturbance distributions) to patient cohorts, consistently outperforming traditional MPC with state estimation.
