ArticleNeuropsychiatric disease and treatment2024
Risk Prediction Model for Non-Suicidal Self-Injury in Chinese Adolescents with Major Depressive Disorder Based on Machine Learning.
Article in Neuropsychiatric disease and treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Can machine learning predict non-suicidal self-injury? A systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Integrating neurobiological markers to prospectively predict adolescent non-suicidal self-injury and suicide attempts: a machine learning approach.Child and adolescent psychiatry and mental health · 2026Article
- Psychopathology profiles and longitudinal correlates of nonsuicidal self-injury in youth: a machine-learning approach.Translational psychiatry · 2026Article
- Construction and internal temporal validation of a LASSO regression-based risk assessment model for non-suicidal self-injury addiction-like features in adolescents and young adults with depression.Frontiers in psychology · 2026Article
- Predictive value of antioxidant and thyroid function indicators for non-suicidal self-injury in adolescents with major depressive disorder.Frontiers in psychiatry · 2026Article
- Visualized Analysis of Adolescent Non-Suicidal Self-Injury and Comorbidity Networks.Behavioral sciences (Basel, Switzerland) · 2025Review
- The risk factors for the comorbidity of depression and self-injury in adolescents: a machine learning study.European child & adolescent psychiatry · 2025Article
- The effect of childhood maltreatment on non-suicidal self-injury in adolescents with bipolar disorder: the mediating role of dysfunctional attitudes and the moderating role of social support.Frontiers in psychiatry · 2025Article
- The relationship between cognitive characteristics of irrational parenthood and stigma in female patients with infertility: a potential profile analysis.Revista da Escola de Enfermagem da U S P · 2025Article
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Authors and funding
12 authors.
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Abstract
Background: Non-suicidal self-injury (NSSI) is a significant social issue, especially among adolescents with major depressive disorder (MDD). This study aimed to construct a risk prediction model using machine learning (ML) algorithms, such as XGBoost and random forest, to identify interventions for healthcare professionals working with adolescents with MDD. Methods: This study investigated 488 adolescents with MDD. Adolescents was randomly divided into 75% training set and 25% test set to testify the predictive value of risk prediction model. The prediction model was constructed using XGBoost and random forest algorithms. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, recall, F Score of the two models for comparing the performance of the two models. Results: There were 161 (33.00%) participants having NSSI. Compared without NSSI, there were statistically significant differences in gender (P=0.035), age (P=0.036), depressive symptoms (P=0.042), sleep quality (P=0.030), dysfunctional attitudes (P=0.048), childhood trauma (P=0.046), interpersonal problems (P=0.047), psychoticism (P) (P=0.049), neuroticism (N) (P=0.044), punishing and Severe (F2) (P=0.045) and Overly-intervening and Protecting (M2) (P=0.047) with NSSI. The AUC values for random forest and XGBoost were 0.780 and 0.807, respectively. The top five most important risk predictors identified by both machine learning methods were dysfunctional attitude, childhood trauma, depressive symptoms, F2 and M2. Conclusion: The study demonstrates the suitability of prediction models for predicting NSSI behavior in Chinese adolescents with MDD based on ML. This model improves the assessment of NSSI in adolescents with MDD by health care professionals working. This provides a foundation for focused prevention and interventions by health care professionals working with these adolescents.
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