Recently, spinal epidural neurostimulation is being considered for rehabilitation of persons suffering from partial spinal cord injury. The neurostimulator must be programmed by a neurosurgeon, yet little work has been done to develop rigorous methods for optimally programming the device. We propose an adaptive design to efficiently optimize programming of the neurostimulator based on specified interim evaluations of patient reported preferences. Preferences for the eligible device configurations are estimated after each interim analysis through a conditionally auto-regressive model that assumes preference for one configuration is related to preferences for neighboring configurations. Using the adaptively updated preferences, a group of configurations is programmed into the device for the patient to evaluate during the next follow-up period. This selection is based on a balance of device exploration and preference maximization. We repeat this process until a specified stopping rule or the calibration-end is reached. We show simulation studies to evaluate the overall quality of the adaptive calibration for various configuration selection strategies and the effects of stopping it early. This article is protected by copyright. All rights reserved.

中文翻译：

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用于优化神经刺激器的批量贝叶斯优化设计

最近，正在考虑将脊髓硬膜外神经刺激用于患有部分脊髓损伤的人的康复。神经刺激器必须由神经外科医生进行编程，但几乎没有做任何工作来开发严格的方法来对设备进行最佳编程。我们提出了一种自适应设计，以根据患者报告的偏好的指定中期评估有效地优化神经刺激器的编程。在每次中期分析之后，通过条件自回归模型估计对合格设备配置的偏好，该模型假定对一种配置的偏好与对相邻配置的偏好相关。使用自适应更新的偏好，一组配置被编程到设备中，供患者在下一个随访期间进行评估。此选择基于设备探索和偏好最大化的平衡。我们重复这个过程，直到达到指定的停止规则或校准结束。我们展示了模拟研究来评估各种配置选择策略的自适应校准的整体质量以及提前停止它的影响。本文受版权保护。版权所有。