1. Risk Prediction Model for Non-Suicidal Self-Injury in Chinese Adolescents with Major Depressive Disorder Based on Machine Learning
- Author
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Sun,Ting, Liu,Jingfang, Wang,Hui, Yang,Bing Xiang, Liu,Zhongchun, Liu,Jie, Wan,Zhiying, Li,Yinglin, Xie,Xiangying, Li,Xiaofen, Gong,Xuan, Cai,Zhongxiang, Sun,Ting, Liu,Jingfang, Wang,Hui, Yang,Bing Xiang, Liu,Zhongchun, Liu,Jie, Wan,Zhiying, Li,Yinglin, Xie,Xiangying, Li,Xiaofen, Gong,Xuan, and Cai,Zhongxiang
- Abstract
Ting Sun,1,2,* Jingfang Liu,3,* Hui Wang,3,* Bing Xiang Yang,3â 5 Zhongchun Liu,3 Jie Liu,6 Zhiying Wan,3 Yinglin Li,1 Xiangying Xie,1 Xiaofen Li,3 Xuan Gong,3 Zhongxiang Cai1 1Department of Nursing, Renmin Hospital of Wuhan University, Wuhan, Peopleâs Republic of China; 2Health Science Center, Yangtze University, Jingzhou, Peopleâs Republic of China; 3Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Peopleâs Republic of China; 4School of Nursing, Wuhan University, Wuhan, Peopleâs Republic of China; 5Population and Health Research Center, Wuhan University, Wuhan, Peopleâs Republic of China; 6Anesthesiology, Virginia Commonwealth University Health System, Richmond, VA, USA*These authors contributed equally to this workCorrespondence: Zhongxiang Cai, Department of Nursing, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuhan, Hubei Province, 430060, Peopleâs Republic of China, Email tg20201228@163.com Xuan Gong, Department of Psychiatry, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuhan, Hubei Province, 430060, Peopleâs Republic of China, Email 12048387@qq.comBackground: 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.Res
- Published
- 2024