Machine learning-based discrimination of unipolar depression and bipolar disorder with streamlined shortlist in adolescents of different ages.

Journal: Computers in biology and medicine
PMID: 39288554

Abstract

BACKGROUND: Variations in symptoms and indistinguishable depression episodes of unipolar depression (UD) and bipolar disorder (BD) make the discrimination difficult and time-consuming. For adolescents with high disease prevalence, an efficient diagnostic tool is important for the discrimination and treatment of BU and UD.

Authors

  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Cheng Huang
    James H. Clark Center, Stanford University, Stanford, California, USA.
  • Pingping Li
    Reproductive Medical Center of Gynecology and Obstetrics Department, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
  • Ben Niu
  • Tingxuan Fan
    Greater Bay Area International Institute for Innovations, Shenzhen University, Shenzhen, China.
  • Hairong Wang
    College of Computing, Georgia Institute of Technology, Atlanta, GA, USA.
  • Yongjie Zhou
    Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China; The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China; Jiangxi Clinical Research Center for Cancer, Nanchang, China.
  • Yujuan Chai
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China. Electronic address: chaiyj@szu.edu.cn.

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