Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection
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.
Authors
- Z. Liu (ORCID: https://orcid.org/0000-0001-6091-884X)
- Soo Peter Ouyang (ORCID: https://orcid.org/0000-0003-1761-5733)
- Wen‐Jing Yan (ORCID: https://orcid.org/0000-0001-6413-2414)
- Dongwu Xu
- Qin Dong (ORCID: https://orcid.org/0000-0003-0494-0270)
- Rui Xu (ORCID: https://orcid.org/0009-0009-8393-4135)
Institutions
- Wenzhou Medical University (CN)
- Jinzhou Kangning Hospital (CN)
Publication Details
- Journal
- BMC Psychiatry
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1186/s12888-025-06693-8
- Citations
- 2
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 4.24
Funders
- National Social Science Fund of China