Research on prediction model of adolescent suicide and self-injury behavior based on machine learning algorithm
The detection rate of suicidal and self-injurious behaviors is higher in women than in men. Adolescents displaying impulsiveness, psychoticism, neuroticism, interpersonal sensitivity, depression, and paranoia have a greater likelihood of engaging in such behaviors. The machine learning model for classifying and predicting adolescent suicide and self-injury risk effectively identifies these behaviors, enabling targeted interventions.
Authors
- Wo Wang (ORCID: https://orcid.org/0009-0002-9442-2097)
- Xiao‐Ming Xu (ORCID: https://orcid.org/0000-0002-7229-0081)
- Yao Gan
- Hong Su (ORCID: https://orcid.org/0000-0002-4673-6449)
- Ming Ai
- Jian Mei Chen
- Qi Zhang (ORCID: https://orcid.org/0000-0003-3617-5892)
- Li Kuang (ORCID: https://orcid.org/0000-0002-3265-2252)
- Jun Cao
- Jing-Lan He (ORCID: https://orcid.org/0000-0003-0217-1287)
Institutions
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-03-06
- DOI
- https://doi.org/10.3389/fpsyt.2024.1521025
- Citations
- 6
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 13.84
Funders
- National Natural Science Foundation of China
- Chongqing Science and Technology Commission