Using machine learning to develop a five-item short form of the children's depression inventory.

Journal: BMC public health
PMID: 38654267

Abstract

BACKGROUND: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention.

Authors

  • Shumei Lin
    College of Psychology, Sichuan Normal University, Chengdu, Sichuan, China.
  • Chengwei Wang
    Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China.
  • Xiuyu Jiang
    College of Psychology, Sichuan Normal University, Chengdu, Sichuan, China.
  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.
  • Dan Luo
    Shimadzu (China) Co., Ltd, Wuhan 430022, China.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Junyi Li
    School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China. Electronic address: lijunyi@hit.edu.cn.
  • Jiajun Xu

Keywords

External Resources

Popular Topics
Recent Journals