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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Vydáno v:BMC psychiatry Ročník 25; číslo 1; s. 286 - 20
Hlavní autoři: Xu, Ruo-Fei, Liu, Zhen-Jing, Ouyang, Shunan, Dong, Qin, Yan, Wen-Jing, Xu, Dong-Wu
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 26.03.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
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
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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 through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. Results The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R 2  = 0.957). This stratified approach demonstrated robust generalizability across age groups (R 2  > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3–0.6). Conclusions This study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation.
AbstractList ObjectiveTo 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.MethodsData 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 through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples.ResultsThe resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R2 = 0.957). This stratified approach demonstrated robust generalizability across age groups (R2 > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3–0.6).ConclusionsThis study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation.
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 through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. Results The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R2 = 0.957). This stratified approach demonstrated robust generalizability across age groups (R2 > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3–0.6). Conclusions This study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation.
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.OBJECTIVETo 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.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 through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples.METHODSData 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 through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples.The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R2 = 0.957). This stratified approach demonstrated robust generalizability across age groups (R2 > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3-0.6).RESULTSThe resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R2 = 0.957). This stratified approach demonstrated robust generalizability across age groups (R2 > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3-0.6).This study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation.CONCLUSIONSThis study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation.
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. 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 through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R  = 0.957). This stratified approach demonstrated robust generalizability across age groups (R  > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3-0.6). This study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation.
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 [greater than or equal to] 16). Model performance was systematically evaluated through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. Results The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R.sup.2 = 0.957). This stratified approach demonstrated robust generalizability across age groups (R.sup.2 > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3-0.6). Conclusions This study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation. Keywords: Depression screening, Stratified screening, Machine learning, CES-D, Recursive Feature Elimination, Predictive modeling
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. 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 [greater than or equal to] 16). Model performance was systematically evaluated through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R.sup.2 = 0.957). This stratified approach demonstrated robust generalizability across age groups (R.sup.2 > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3-0.6). This study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation.
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 through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. Results The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. Together, these nine items enable accurate prediction of the full CES-D-20 total score (R 2  = 0.957). This stratified approach demonstrated robust generalizability across age groups (R 2  > 0.94, accuracy > 0.91) and time points. Calibration analyses and decision curve analyses confirmed optimal clinical utility, particularly in the critical risk threshold range (0.3–0.6). Conclusions This study contributes to the refinement of CES-D by developing a machine learning-derived stratified screening version, offering an efficient and reliable approach that optimizes assessment burden while maintaining excellent psychometric properties. The stratified design makes it particularly valuable for large-scale mental health screening programs, enabling efficient risk stratification and targeted assessment allocation.
ArticleNumber 286
Audience Academic
Author Xu, Dong-Wu
Ouyang, Shunan
Dong, Qin
Yan, Wen-Jing
Xu, Ruo-Fei
Liu, Zhen-Jing
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/40133848$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1146/annurev-clinpsy-050212-185608
10.1186/s12889-024-20699-z
10.1186/s12916-015-0325-4
10.1371/journal.pone.0155431
10.1126/science.3287615
10.1146/annurev.clinpsy.032408.153553
10.1016/j.psychres.2010.08.018
10.3390/diagnostics13010002
10.1016/s0749-3797(18)30622-6
10.1177/1745691617693393
10.1002/9780470061572.eqr173
10.1002/wps.20375
10.1186/s13034-023-00574-2
10.1037/abn0000002
10.1177/1745691620902467
10.1007/s11136-009-9560-5
10.1016/j.socscimed.2010.03.035
10.1097/01.MLR.0000093487.78664.3C
10.1146/annurev-clinpsy-021815-093634
10.1002/wps.20569
10.1080/15374416.2018.1437735
10.1016/j.jad.2024.09.111
10.1177/014662167700100306
10.1016/j.genhosppsych.2024.01.010
10.1186/s12888-016-1178-x
10.1016/S0167-5877(00)00115-X
10.3389/fpsyt.2022.921781
10.1186/s40345-024-00365-4
10.18637/jss.v071.i05
10.1186/s40359-024-01702-z
10.1016/j.jad.2024.08.121
10.1007/s11336-010-9178-0
10.1080/00031305.1985.10479448
10.1037/1082-989x.5.2.155
10.1007/s00127-016-1319-z
10.1080/00223891.2010.496477
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Issue 1
Keywords CES-D
Predictive modeling
Recursive Feature Elimination
Depression screening
Machine learning
Stratified screening
Language English
License 2025. The Author(s).
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References EI Fried (6693_CR22) 2017; 52
D Borsboom (6693_CR29) 2017; 16
M Zimmerman (6693_CR31) 2018; 17
MC Mullarkey (6693_CR25) 2018; 48
6693_CR14
DJ Robinaugh (6693_CR28) 2014; 123
6693_CR35
6693_CR34
F Ni (6693_CR36) 2024; 12
TO Kvålseth (6693_CR13) 1985; 39
H Ding (6693_CR12) 2024
NT Van Dam (6693_CR2) 2010; 186
G Vilagut (6693_CR3) 2016; 11
W Yan (6693_CR8) 2023; 13
SP Reise (6693_CR20) 2010; 92
SP Reise (6693_CR21) 2009; 5
LS Radloff (6693_CR1) 1977; 1
SW Choi (6693_CR32) 2009; 19
Z Yu (6693_CR10) 2024; 366
G Shen (6693_CR11) 2024; 12
RD Gibbons (6693_CR30) 2016; 12
K Kroenke (6693_CR7) 2003; 41
EC Baron (6693_CR6) 2017; 17
JR Edwards (6693_CR19) 2000; 5
Q Ruan (6693_CR27) 2023; 17
6693_CR26
EM Andresen (6693_CR4) 1994; 10
D Borsboom (6693_CR23) 2013; 9
T Yarkoni (6693_CR18) 2017; 12
R Jacobucci (6693_CR17) 2020; 15
S Van De Velde (6693_CR5) 2010; 71
EI Fried (6693_CR24) 2015; 13
M Greiner (6693_CR16) 2000; 45
JA Swets (6693_CR15) 1988; 240
F Wang (6693_CR9) 2024; 87
L Cai (6693_CR33) 2010; 75
References_xml – volume: 9
  start-page: 91
  issue: 1
  year: 2013
  ident: 6693_CR23
  publication-title: Annu Rev Clin Psychol
  doi: 10.1146/annurev-clinpsy-050212-185608
– ident: 6693_CR35
  doi: 10.1186/s12889-024-20699-z
– volume: 13
  start-page: 1
  year: 2015
  ident: 6693_CR24
  publication-title: BMC Med
  doi: 10.1186/s12916-015-0325-4
– volume: 11
  start-page: e0155431
  issue: 5
  year: 2016
  ident: 6693_CR3
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0155431
– volume: 240
  start-page: 1285
  issue: 4857
  year: 1988
  ident: 6693_CR15
  publication-title: Science
  doi: 10.1126/science.3287615
– volume: 5
  start-page: 27
  issue: 1
  year: 2009
  ident: 6693_CR21
  publication-title: Annu Rev Clin Psychol
  doi: 10.1146/annurev.clinpsy.032408.153553
– volume: 186
  start-page: 128
  issue: 1
  year: 2010
  ident: 6693_CR2
  publication-title: Psychiatry Res
  doi: 10.1016/j.psychres.2010.08.018
– volume: 13
  start-page: 2
  issue: 1
  year: 2023
  ident: 6693_CR8
  publication-title: Diagnostics
  doi: 10.3390/diagnostics13010002
– volume: 10
  start-page: 77
  issue: 2
  year: 1994
  ident: 6693_CR4
  publication-title: Am J Prev Med
  doi: 10.1016/s0749-3797(18)30622-6
– volume: 12
  start-page: 1100
  issue: 6
  year: 2017
  ident: 6693_CR18
  publication-title: Perspectives on psychological science : a journal of the Association for Psychological Science
  doi: 10.1177/1745691617693393
– ident: 6693_CR14
  doi: 10.1002/9780470061572.eqr173
– volume: 16
  start-page: 5
  issue: 1
  year: 2017
  ident: 6693_CR29
  publication-title: World Psychiatry
  doi: 10.1002/wps.20375
– volume: 17
  start-page: 29
  issue: 1
  year: 2023
  ident: 6693_CR27
  publication-title: Child and Adolescent Psychiatry and Mental Health
  doi: 10.1186/s13034-023-00574-2
– volume: 123
  start-page: 510
  issue: 3
  year: 2014
  ident: 6693_CR28
  publication-title: J Abnorm Psychol
  doi: 10.1037/abn0000002
– volume: 15
  start-page: 809
  issue: 3
  year: 2020
  ident: 6693_CR17
  publication-title: Perspect Psychol Sci
  doi: 10.1177/1745691620902467
– volume: 19
  start-page: 125
  issue: 1
  year: 2009
  ident: 6693_CR32
  publication-title: Qual Life Res
  doi: 10.1007/s11136-009-9560-5
– volume: 71
  start-page: 305
  issue: 2
  year: 2010
  ident: 6693_CR5
  publication-title: Social Science and Medicine
  doi: 10.1016/j.socscimed.2010.03.035
– volume: 41
  start-page: 1284
  issue: 11
  year: 2003
  ident: 6693_CR7
  publication-title: Med Care
  doi: 10.1097/01.MLR.0000093487.78664.3C
– volume: 12
  start-page: 83
  issue: 1
  year: 2016
  ident: 6693_CR30
  publication-title: Annu Rev Clin Psychol
  doi: 10.1146/annurev-clinpsy-021815-093634
– volume: 17
  start-page: 258
  issue: 3
  year: 2018
  ident: 6693_CR31
  publication-title: World Psychiatry
  doi: 10.1002/wps.20569
– volume: 48
  start-page: 656
  issue: 4
  year: 2018
  ident: 6693_CR25
  publication-title: J Clin Child Adolesc Psychol
  doi: 10.1080/15374416.2018.1437735
– year: 2024
  ident: 6693_CR12
  publication-title: J Affect Disord
  doi: 10.1016/j.jad.2024.09.111
– volume: 1
  start-page: 385
  issue: 3
  year: 1977
  ident: 6693_CR1
  publication-title: Appl Psychol Meas
  doi: 10.1177/014662167700100306
– volume: 87
  start-page: 33
  year: 2024
  ident: 6693_CR9
  publication-title: Gen Hosp Psychiatry
  doi: 10.1016/j.genhosppsych.2024.01.010
– volume: 17
  start-page: 6
  issue: 1
  year: 2017
  ident: 6693_CR6
  publication-title: BMC Psychiatry
  doi: 10.1186/s12888-016-1178-x
– volume: 45
  start-page: 23
  issue: 1–2
  year: 2000
  ident: 6693_CR16
  publication-title: Prev Vet Med
  doi: 10.1016/S0167-5877(00)00115-X
– ident: 6693_CR26
  doi: 10.3389/fpsyt.2022.921781
– volume: 12
  start-page: 42
  issue: 1
  year: 2024
  ident: 6693_CR11
  publication-title: International Journal of Bipolar Disorders
  doi: 10.1186/s40345-024-00365-4
– ident: 6693_CR34
  doi: 10.18637/jss.v071.i05
– volume: 12
  start-page: 213
  issue: 1
  year: 2024
  ident: 6693_CR36
  publication-title: BMC Psychology
  doi: 10.1186/s40359-024-01702-z
– volume: 366
  start-page: 181
  year: 2024
  ident: 6693_CR10
  publication-title: J Affect Disord
  doi: 10.1016/j.jad.2024.08.121
– volume: 75
  start-page: 581
  issue: 4
  year: 2010
  ident: 6693_CR33
  publication-title: Psychometrika
  doi: 10.1007/s11336-010-9178-0
– volume: 39
  start-page: 279
  issue: 4
  year: 1985
  ident: 6693_CR13
  publication-title: Am Stat
  doi: 10.1080/00031305.1985.10479448
– volume: 5
  start-page: 155
  issue: 2
  year: 2000
  ident: 6693_CR19
  publication-title: Psychol Methods
  doi: 10.1037/1082-989x.5.2.155
– volume: 52
  start-page: 1
  year: 2017
  ident: 6693_CR22
  publication-title: Soc Psychiatry Psychiatr Epidemiol
  doi: 10.1007/s00127-016-1319-z
– volume: 92
  start-page: 544
  issue: 6
  year: 2010
  ident: 6693_CR20
  publication-title: J Pers Assess
  doi: 10.1080/00223891.2010.496477
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Snippet Objective To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while...
To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while...
Objective To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while...
ObjectiveTo develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while...
Abstract Objective To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale...
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StartPage 286
SubjectTerms 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
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Title Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection
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