Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis.

Journal: Frontiers in psychiatry
Published Date: Jan 28, 2025

Abstract

BACKGROUND: Diagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization of artificial intelligence in mental health, researchers are endeavoring to create AI-based diagnostic models. In this context, some researchers have sought to develop machine learning models for bipolar disorder diagnosis. Nevertheless, the accuracy of these diagnoses remains a subject of controversy. Consequently, we conducted this systematic review to comprehensively assess the diagnostic value of machine learning in the context of bipolar disorder.

Authors

  • Yi Pan
    Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.
  • Pushi Wang
    Department of Mental Disorders, National Center for Mental Health, NCMHC, Beijing, China.
  • Bowen Xue
    Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Yanbin Liu
    Department of Mental Disorders, National Center for Mental Health, NCMHC, Beijing, China.
  • Xinhua Shen
    Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.
  • Shiliang Wang
    Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.
  • Xing Wang
    Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.

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