A predictive model of non-suicidal self-injury - a study based on the construction and validation of a nomogram
BackgroundThe issue of psychological maladjustment, particularly Non-Suicidal Self-Injury (NSSI), is prevalent among vocational high school students. Therefore, timely identification of high-risk individuals is important in providing further intervention.MethodsA survey was conducted among 2081 stud...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1539884/full |
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| author | YuJie Liu YuJie Liu TaiMin Wu Shu Yan Yang Zhou Lianzhong Liu |
| author_facet | YuJie Liu YuJie Liu TaiMin Wu Shu Yan Yang Zhou Lianzhong Liu |
| author_sort | YuJie Liu |
| collection | DOAJ |
| description | BackgroundThe issue of psychological maladjustment, particularly Non-Suicidal Self-Injury (NSSI), is prevalent among vocational high school students. Therefore, timely identification of high-risk individuals is important in providing further intervention.MethodsA survey was conducted among 2081 students from a vocational high school in Wuhan, China. The students were divided into two groups: those who had engaged in Non-Suicidal Self-Injury (NSSI) within the past two weeks and those who had not. Lasso regression and logistic regression were employed to identify significant risk factors associated with NSSI. Subsequently, a nomogram was developed to enhance the accuracy and efficiency of identifying individuals at high risk for NSSI. The performance of the model was assessed through various validation methods including Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA).ResultsThe significant predictors of NSSI encompassed gender, problem behavior, depressive mood, and borderline personality tendencies. Based on these predictors, a nomogram was constructed. The model’s accuracy was validated using AUC, calibration curves, and DCA, showing high accuracy.ConclusionA nomogram prediction tool for NSSI among vocational high school students was constructed, providing an accurate and quick method for predicting adolescent NSSI behavior. |
| format | Article |
| id | doaj-art-b5adc89563844940a36dfa690a13bb11 |
| institution | DOAJ |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-b5adc89563844940a36dfa690a13bb112025-08-20T03:07:01ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-04-011610.3389/fpsyt.2025.15398841539884A predictive model of non-suicidal self-injury - a study based on the construction and validation of a nomogramYuJie Liu0YuJie Liu1TaiMin Wu2Shu Yan3Yang Zhou4Lianzhong Liu5School of Medicine, Jianghan University, Wuhan, Hubei, ChinaOffice of Psychosocial Services, Wuhan Mental Health Center, Wuhan, Hubei, ChinaOffice of Psychosocial Services, Wuhan Mental Health Center, Wuhan, Hubei, ChinaOffice of Psychosocial Services, Wuhan Mental Health Center, Wuhan, Hubei, ChinaOffice of Psychosocial Services, Wuhan Mental Health Center, Wuhan, Hubei, ChinaDepartment of Psychiatry, Wudong Hospital, Wuhan, Hubei, ChinaBackgroundThe issue of psychological maladjustment, particularly Non-Suicidal Self-Injury (NSSI), is prevalent among vocational high school students. Therefore, timely identification of high-risk individuals is important in providing further intervention.MethodsA survey was conducted among 2081 students from a vocational high school in Wuhan, China. The students were divided into two groups: those who had engaged in Non-Suicidal Self-Injury (NSSI) within the past two weeks and those who had not. Lasso regression and logistic regression were employed to identify significant risk factors associated with NSSI. Subsequently, a nomogram was developed to enhance the accuracy and efficiency of identifying individuals at high risk for NSSI. The performance of the model was assessed through various validation methods including Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA).ResultsThe significant predictors of NSSI encompassed gender, problem behavior, depressive mood, and borderline personality tendencies. Based on these predictors, a nomogram was constructed. The model’s accuracy was validated using AUC, calibration curves, and DCA, showing high accuracy.ConclusionA nomogram prediction tool for NSSI among vocational high school students was constructed, providing an accurate and quick method for predicting adolescent NSSI behavior.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1539884/fullmachine learningneural networksdata analysisbioinformaticsclinical applications |
| spellingShingle | YuJie Liu YuJie Liu TaiMin Wu Shu Yan Yang Zhou Lianzhong Liu A predictive model of non-suicidal self-injury - a study based on the construction and validation of a nomogram Frontiers in Psychiatry machine learning neural networks data analysis bioinformatics clinical applications |
| title | A predictive model of non-suicidal self-injury - a study based on the construction and validation of a nomogram |
| title_full | A predictive model of non-suicidal self-injury - a study based on the construction and validation of a nomogram |
| title_fullStr | A predictive model of non-suicidal self-injury - a study based on the construction and validation of a nomogram |
| title_full_unstemmed | A predictive model of non-suicidal self-injury - a study based on the construction and validation of a nomogram |
| title_short | A predictive model of non-suicidal self-injury - a study based on the construction and validation of a nomogram |
| title_sort | predictive model of non suicidal self injury a study based on the construction and validation of a nomogram |
| topic | machine learning neural networks data analysis bioinformatics clinical applications |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1539884/full |
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