ArticleWorld journal of psychiatry2026
Gradient boosting machine model predicts psychiatric complications after deep brain stimulation in Parkinson's disease.
Article in World journal of psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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- Various machine learning prediction models for short-term postoperative prognosis of colorectal cancer based on intraoperative hypothermia.American journal of cancer research · 2026Article
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Abstract
backgroundDeep brain stimulation (DBS) is an effective method for treating the motor symptoms of advanced Parkinson's disease (PD). However, complications such as depression, anxiety, cognitive impairment, and delirium that occur after DBS surgery can severely affect a patient's quality of life and the therapeutic effect.
aimTo construct a gradient boosting machine (GBM) risk model to predict the risk of mental complications such as depression, anxiety, and cognitive impairment in patients with PD after DBS surgery.
methodsWe retrospectively collected data on patients with PD treated at a top-tier hospital in China between June 2023 and December 2024. During this period, 234 cases were screened and analyzed, of which 70% were included in the modeling set and the remaining 30% in the test set. The modeling set was used to construct the risk prediction model, whereas the test set was used to validate the predictive performance of the model. Additionally, we used the GBM model to predict outcomes for 65 patients with PD who visited the hospital between January 2025 and April 2025, and analyzed the application effect of the model.
resultsIn a cohort of 234 patients undergoing DBS, the incidence of psychiatric complications such as depression, anxiety, and cognitive impairment was 37.61%. Age, surgery duration, fasting time, family relationship health assessment scale scores, and unified PD rating scale III scores were identified as independent influencing factors. Based on these variables, the constructed GBM model demonstrated excellent predictive performance, with an accuracy of 80.0%, sensitivity of 95.7%, and specificity of 78.6%. Decision curve analysis revealed that the model's clinical benefit and applicability are optimal when the threshold is between 0.09 and 0.70.
conclusionThe prediction model constructed based on the GBM algorithm has good predictive performance and can provide a reference for clinical medical staff to identify groups at high risk for mental complications such as depression, anxiety, cognitive impairment, and delirium after DBS.
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