Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features.

Journal: BMC public health
PMID: 39844104

Abstract

BACKGROUND: Depressive symptoms in adolescents can significantly affect their daily lives and pose risks to their future development. These symptoms may be linked to various factors experienced during both childhood and adolescence. Machine learning (ML) has attracted substantial attention in the field of adolescent depression; however, studies establishing prediction models have primarily considered childhood or adolescent features separately, resulting in a lack of analyses that incorporate factors from both stages.

Authors

  • Xinzhu Liu
    Department of Health and Intelligent Engineering, College of Health Management, China Medical University, 110122, Shenyang, Liaoning Province, China.
  • Rui Cang
    Department of Health and Intelligent Engineering, College of Health Management, China Medical University, 110122, Shenyang, Liaoning Province, China.
  • Zihe Zhang
    Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL 35487, United States. Electronic address: zzhang124@crimson.ua.edu.
  • Ping Li
    Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Hui Wu
    China Medical University College of Health Management, Shenyang 110122, Liaoning Province, China.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
  • Shu Li
    China Medical University College of Health Management, Shenyang 110122, Liaoning Province, China.

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