scholarly journals Incorporating Ensemble approach into Automated Machine Learning in an attempt to predict the Risk of Postoperative Delirium After Deep Brain Stimulation Surgery in Parkinson's Disease Patients : Secondary analysis of a retrospective cohort study

2021 ◽  
Author(s):  
Ali Haider Bangash ◽  
Tauseef Ullah ◽  
Inayat Ullah Khan ◽  
Arshiya Fatima ◽  
Saiqa Zehra
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Postoperative Delirium ◽  
Secondary Analysis ◽  
Automated Machine Learning ◽  
Risk Predictor ◽  
Deep Brain

Automated machine learning is explored to develop a sensitive risk predictor for postoperative delirium in elderly Parkinson's Disease patients who have received deep brain stimulation surgical intervention.

scholarly journals Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alexandre Boutet ◽  
Radhika Madhavan ◽  
Gavin J. B. Elias ◽  
Suresh E. Joel ◽  
Robert Gramer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
A Priori ◽  
Brain Response ◽  
Brain Responses ◽  
Clinical Benefits ◽  
Deep Brain

AbstractCommonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming.

Postoperative delirium in Parkinson’s disease patients following deep brain stimulation surgery

2014 ◽  
Vol 21 (7) ◽  
pp. 1192-1195 ◽  
Author(s):  
Jonathan D. Carlson ◽  
Joshua J. Neumiller ◽  
Lindy D.W. Swain ◽  
Jamie Mark ◽  
Pam McLeod ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Postoperative Delirium ◽  
Deep Brain Stimulation Surgery ◽  
Deep Brain

Nomogram for Predicting Postoperative Delirium After Deep Brain Stimulation Surgery for Parkinson's Disease

2019 ◽  
Vol 130 ◽  
pp. e551-e557 ◽  
Author(s):  
Xue-qi Wang ◽  
Hong-xia Zhuang ◽  
Li-xiang Zhang ◽  
Xia Chen ◽  
Chao-shi Niu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Postoperative Delirium ◽  
Deep Brain Stimulation Surgery ◽  
Deep Brain
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