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Activity Number:
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193
- Section on Medical Devices and Diagnostics: Student Paper Competition
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Medical Devices and Diagnostics
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Abstract #321017
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Title:
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Batch Bayesian Optimization Design for Optimizing a Neurostimulator
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Author(s):
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Adam Kaplan * and Thomas Murray
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Companies:
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Center for Care Delivery and Outcomes Research and University of Minnesota
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Keywords:
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adaptive design ;
medical device ;
n-of-1 trial;
personalized medicine;
spinal-cord injury ;
batch bayesian optimization
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Abstract:
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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 configuration 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 patient to evaluate during the next follow-up period. The 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.
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Authors who are presenting talks have a * after their name.
