Prediction of post stroke depression with machine learning: A national multicenter cohort study.

Journal: Journal of psychiatric research
Published Date: May 6, 2025

Abstract

OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis. Most existing PSD prediction models suffer from incomplete data inclusion, which limits their clinical predictive value. This study aims to integrate multimodal data, including clinical characteristics, biomarkers, and neuroimaging variables, to validate the potential of machine learning models in efficiently identifying high-risk PSD patients.

Authors

  • Yumeng Gu
    Department of Neurology, Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
  • Juanjuan Xue
    Department of Neurology, Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
  • Xiaoshuang Xia
    Department of Neurology, Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
  • Xiaokun Guo
    Department of Health and Medical &Geriatrics, Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
  • Zhongyan Wang
    Center for Data Science, New York University, New York, USA.
  • Kun Wu
    Image Processing Center, School of Astronautics, Beihang University, Beijing, 102206, China.
  • Wei Yue
    School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.
  • Nian Chen
    Department of Neurology, Ninghe District Hospital, Tianjin, 301599, China.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.

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