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Machine learning explains response variability of deep brain stimulation on Parkinson's disease quality of life

Medicine and Health

Machine learning explains response variability of deep brain stimulation on Parkinson's disease quality of life

E. Ferrea, F. Negahbani, et al.

Discover how explainable machine learning uncovers key predictors of quality of life changes in Parkinson's disease patients after deep brain stimulation. This research, conducted by Enrico Ferrea, Farzin Negahbani, Idil Cebi, Daniel Weiss, and Alireza Gharabaghi, reveals that preoperative factors and brain activity significantly influence patient outcomes.

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Playback language: English
Abstract
This study used explainable machine learning to analyze multimodal factors affecting quality of life (QoL) changes after deep brain stimulation (DBS) in Parkinson's disease patients. Preoperative PDQ-39 scores and upper beta band activity in the left STN were key predictors of QoL changes. Lower initial QoL predicted worsening, while improvement was associated with higher beta activity. Electrode positions relative to the z = -7 coordinate influenced outcomes.
Publisher
npj Digital Medicine
Published On
Oct 02, 2024
Authors
Enrico Ferrea, Farzin Negahbani, Idil Cebi, Daniel Weiss, Alireza Gharabaghi
Tags
Parkinson's disease
deep brain stimulation
quality of life
machine learning
predictors
beta band activity
electrode positioning

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