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Investigating Risk Factors And Predicting Complications In Deep Brain Stimulation Surgery With Machine Learning Algorithms, Farrokh Farrokhi, Quinlan D. Buchlak, Matt Sikora, Nazanin Esmaili, Maria Marsans, Pamela Mcleod, Jamie Mark, Emily Cox, Christine Bennett, Jonathan Carlson
Investigating Risk Factors And Predicting Complications In Deep Brain Stimulation Surgery With Machine Learning Algorithms, Farrokh Farrokhi, Quinlan D. Buchlak, Matt Sikora, Nazanin Esmaili, Maria Marsans, Pamela Mcleod, Jamie Mark, Emily Cox, Christine Bennett, Jonathan Carlson
Medical Papers and Journal Articles
Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to (1) investigate preoperative clinical risk factors, and (2) build machine learning models to predict adverse outcomes.
Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n=501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% …