{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xu RF"],"funding":["Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions","National Social Science Fund of China"],"pagination":["286"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11938587"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["Objective
To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while maintaining diagnostic accuracy, addressing the efficiency limitations in large-scale applications.Methods
Data were derived from the Chinese Psychological Health Guard Project (primary sample: n = 179,877; age 9-18) and China Labor-force Dynamics Survey (validation samples across age spans). We employed a two-stage machine learning approach: first applying Recursive Feature Elimination with multiple linear regression to identify core predictive items for total depression scores, followed by logistic regression for optimizing depression classification (CES-D ≥ 16). Model performance was systematically evaluated throu"],"journal":["BMC psychiatry"],"pubmed_title":["Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection."],"pmcid":["PMC11938587"],"funding_grant_id":["2024GH036","23BSH135"],"pubmed_authors":["Ouyang S","Liu ZJ","Yan WJ","Xu RF","Xu DW","Dong Q"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.","description":"Objective
To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while maintaining diagnostic accuracy, addressing the efficiency limitations in large-scale applications.Methods
Data were derived from the Chinese Psychological Health Guard Project (primary sample: n = 179,877; age 9-18) and China Labor-force Dynamics Survey (validation samples across age spans). We employed a two-stage machine learning approach: first applying Recursive Feature Elimination with multiple linear regression to identify core predictive items for total depression scores, followed by logistic regression for optimizing depression classification (CES-D ≥ 16). Model performance was systematically evaluated throu","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-07-04T03:05:42.976Z","creation":"2025-07-04T03:05:42.976Z"},"accession":"S-EPMC11938587","cross_references":{"pubmed":["40133848"],"doi":["10.1186/s12888-025-06693-8"]}}