Decoding the adolescent non-suicidal self-injury: understanding with interpretable machine learning insights
Leveraging multiple machine learning algorithms for a holistic item analysis, this research identifies critical risk and protective factors for non-suicidal self-injury, thus refining the Integrated Theoretical Model.
Authors
- Haojie Fu (ORCID: https://orcid.org/0000-0003-1547-7250)
- Shuran Yang (ORCID: https://orcid.org/0000-0001-9143-0900)
- Xudong Zhao (ORCID: https://orcid.org/0000-0003-2272-6278)
- Chuanyuan Kang (ORCID: https://orcid.org/0009-0007-7382-7649)
- Liang Liu (ORCID: https://orcid.org/0000-0001-7556-4810)
- Mengmeng Zhang (ORCID: https://orcid.org/0000-0002-7017-7093)
Institutions
- Tongji University (CN)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Shanghai Mental Health Center (CN)
- Shanghai East Hospital (CN)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1186/s12889-025-24354-z
- Citations
- 3
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.92
Funders
- Shanghai Municipal Health Commission
- National Social Science Fund of China