Predicting adolescent depressive symptoms using teacher-reported textual descriptions of abnormal behaviors: a study based on machine learning
Machine learning, particularly Random Forest, can effectively predict adolescent depressive symptoms from teacher-reported text. This approach offers a practical and efficient tool for early identification in school settings, facilitating timely intervention.
Authors
- Xiaofei Hou (ORCID: https://orcid.org/0000-0003-0151-6466)
- Guangming Xu
- Minghui Li (ORCID: https://orcid.org/0000-0003-1909-6260)
- Ling Sun
- Guoli Yan
- Huifang Yin
- Jianan Song
- Jiansong Zhou
- Nigela Wumaierjiang
- Lidan Yuan
Institutions
- Central South University (CN)
- Tianjin University (CN)
- Tianjin Anding Hospital (CN)
- National Clinical Research (US)
- Second Xiangya Hospital of Central South University (CN)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-01-08
- DOI
- https://doi.org/10.3389/frai.2025.1732682
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 0.00