Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships.

Journal: Computers in biology and medicine
PMID: 38631118

Abstract

OBJECTIVE: Depression and anxiety, prevalent coexisting mood disorders, pose a clinical challenge in accurate differentiation, hindering effective healthcare interventions. This research addressed this gap by employing a streamlined Symptom Checklist 90 (SCL-90) designed to minimize patient response burden. Utilizing machine learning algorithms, the study sought to construct classification models capable of distinguishing between depression and anxiety.

Authors

  • Tiantian Wang
  • Chuang Xue
    Hangzhou Seventh People's Hospital, Affiliated Mental Health Center, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
  • Zijian Zhang
    School of Physics, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
  • Tingting Cheng
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.

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