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BACKGROUND It is important to diagnose depression in Parkinson’s disease(DPD)as soon as possible and identify the predictors of depression to improve quality of life in Parkinson’s disease(PD)patients.AIM To develop a model for predicting DPD based on the support vector machine,while considering sociodemographic factors,health habits,Parkinson's symptoms,sleep behavior disorders,and neuropsychiatric indicators as predictors and provide baseline data for identifying DPD.METHODS This study analyzed 223 of 335 patients who were 60 years or older with PD.Depression was measured using the 30 items of the Geriatric Depression Scale,and the explanatory variables included PD-related motor signs,rapid eye movement sleep behavior disorders,and neuropsychological tests.The support vector machine was used to develop a DPD prediction model.RESULTS When the effects of PD motor symptoms were compared using“functional weight”,late motor complications(occurrence of levodopa-induced dyskinesia)were the most influential risk factors for Parkinson's symptoms.CONCLUSION It is necessary to develop customized screening tests that can detect DPD in the early stage and continuously monitor high-risk groups based on the factors related to DPD derived from this predictive model in order to maintain the emotional health of PD patients.
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篇名 Development of a depression in Parkinson's disease prediction model using machine learning
来源期刊 世界精神病学杂志 学科 医学
关键词 Depression in Parkinson's disease Supervised Machine Learning Neuropsychological test Risk factor Support vector machine Rapid eye movement sleep behavior disorders
年,卷(期) 2020,(10) 所属期刊栏目
研究方向 页码范围 234-244
页数 11页 分类号 R74
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节点文献
Depression
in
Parkinson's
disease
Supervised
Machine
Learning
Neuropsychological
test
Risk
factor
Support
vector
machine
Rapid
eye
movement
sleep
behavior
disorders
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
世界精神病学杂志
不定期
2220-3206
北京市朝阳区东四环中路62号楼远洋国际中
出版文献量(篇)
31
总下载数(次)
0
总被引数(次)
0
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