ArticleWorld journal of psychiatry2020
Development of a depression in Parkinson's disease prediction model using machine learning.
Article in World journal of psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 38 citations in OpenAlex.
- Study of a Kawasaki disease diagnostic prediction model based on the LightGBM machine learning algorithm.Frontiers in artificial intelligence · 2026Article
- Utilizing combined quantitative multiparametric MRI as potential biomarkers for improved early-stage parkinson's disease diagnosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.CNS neuroscience & therapeutics · 2025Article
- Article
- Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson's disease.Frontiers in aging neuroscience · 2025Article
- A scoping review of neurodegenerative manifestations in explainable digital phenotyping.NPJ Parkinson's disease · 2023Article
- Physical function, ADL, and depressive symptoms in Chinese elderly: Evidence from the CHARLS.Frontiers in public health · 2023Article
- Screening dementia and predicting high dementia risk groups using machine learning.World journal of psychiatry · 2022Article
- A web-based novel prediction model for predicting depression in elderly patients with coronary heart disease: A multicenter retrospective, propensity-score matched study.Frontiers in psychiatry · 2022Article
- Multi-predictor modeling for predicting early Parkinson's disease and non-motor symptoms progression.Frontiers in aging neuroscience · 2022Article
- Developing a nomogram for predicting the depression of senior citizens living alone while focusing on perceived social support.World journal of psychiatry · 2021Article
- Trend of recognizing depression symptoms and antidepressants use in newly diagnosed Parkinson's disease: Population-based study.Brain and behavior · 2021Article
- Predicting the Severity of Parkinson's Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model.International journal of environmental research and public health · 2021Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt 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.
aimTo 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.
methodsThis 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.
resultsWhen 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.
conclusionIt 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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