Batch Bayesian optimization design for optimizing a neurostimulator.

ABSTRACT: 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 autoregressive 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 p

SUBMITTER: Kaplan A 

PROVIDER: S-EPMC7736298 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature