逻辑回归
列线图
焦虑
毒物控制
临床心理学
心理学
多元统计
伤害预防
人口
萧条(经济学)
自杀预防
医学
精神科
环境卫生
机器学习
计算机科学
内科学
宏观经济学
经济
作者
Yunling Zhong,Jinlong He,Jing Luo,Jiayu Zhao,Yu Cen,Yuqin Song,Yuhang Wu,Cen Lin,Lu Pan,Jiaming Luo
标识
DOI:10.1016/j.jad.2023.10.110
摘要
The prevalence of non-suicidal self-injurious (NSSI) in adolescents is high. However, few studies exist to predict NSSI in this population. This study employed a machine learning algorithm to develop a predictive model, aiming to more accurately assess the risk of NSSI in Chinese adolescents. Sociodemographic, psychological data were collected in 50 schools in western China. We constructed eXtreme Gradient Boosting (XGBoost) model and multivariate logistic regression model to predict the risk of NSSI and nomograms are plotted. Data from 13,304 adolescents were used for model development, with an average age of 13.00 ± 2.17 years; 617 individuals (4.6 %) reported non-suicidal self-injury (NSSI) behaviors. The results of the XGBoost model showed that depression and anxiety were the top two predictors of NSSI in adolescents. The results of the multivariate logistic regression model showed that the risk factors for adolescent NSSI behaviors include: gender (being female), Age, Living with whom (father), History of psychiatric consultation, Stress, Depression, Anxiety, Tolerance, Emotion abreaction. The XGBoost prediction and multivariate logistic regression model showed good predictive ability. Nomograms can serve as clinical tools to assist in intervention measures, helping adolescents reduce NSSI behaviors and improve their mental and physical well-being.
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