Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease.

Kenneth H LouieMatthew N PetrucciLogan L GradoChiahao LuPaul J TuiteAndrew G LamperskiColum D MacKinnonScott E CooperTheoden I Netoff

Published in: Journal of neuroengineering and rehabilitation (2021)

These results provide preliminary evidence of the feasibility to use BayesOpt for determining the optimal frequency, while pGP patient's preferences include more difficult to measure outcomes. Both novel approaches can shorten DBS programming and can be expanded to include multiple symptoms and parameters.

Keyphrases

deep brain stimulation
parkinson disease
obsessive compulsive disorder
case report
machine learning
high throughput
deep learning
decision making
skeletal muscle
insulin resistance
single cell
weight loss