| Autor: |
Xiao-Ming Xu, Yang S. Liu, Su Hong, Chuan Liu, Jun Cao, Xiao-Rong Chen, Zhen Lv, Bo Cao, Heng-Guang Wang, Wo Wang, Ming Ai, Li Kuang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of Affective Disorders Reports, Vol 16, Iss , Pp 100723- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-9153 |
| DOI: |
10.1016/j.jadr.2024.100723 |
| Popis: |
Objectives: To enhance the ability of predicting self-harm behaviors through multidimensional data and machine learning methods, and provide a foundation for future comprehensive interventions. Methods: One hundred and twelve young adults aged 18-22 years with self-harm behaviors participated in this study as an experimental group, 98 in the control group. Eighty-three social-demographic and genetic features were collected and analyzed by an extreme gradient boosting (XGBoost) approach. Results: We found significant differences in social-demographic and genetic features between the self-harm and control groups (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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