Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection

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 Chine...

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Bibliographic Details
Published in:	BMC psychiatry Vol. 25; no. 1; pp. 286 - 20
Main Authors:	Xu, Ruo-Fei, Liu, Zhen-Jing, Ouyang, Shunan, Dong, Qin, Yan, Wen-Jing, Xu, Dong-Wu
Format:	Journal Article
Language:	English
Published:	London BioMed Central 26.03.2025 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Accuracy Adolescent Age Algorithms CES-D Child Children & youth China Computational psychiatry Data collection Depression - diagnosis Depression screening Depression, Mental Depressive Disorder - diagnosis Discriminant analysis Epidemiology Feature selection Female Humans Learning algorithms Machine Learning Male Mass Screening - methods Medical screening Medicine Medicine & Public Health Mental depression Mental health Methods Predictive modeling Psychiatric Status Rating Scales Psychiatry Psychological aspects Psychometrics Psychotherapy Quantitative psychology Recursive Feature Elimination Stratified screening Technology application Teenagers China Taiwan CES-D Predictive modeling Recursive Feature Elimination Depression screening Machine learning Stratified screening
ISSN:	1471-244X, 1471-244X
Online Access:	Get full text
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