Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

Journal: International journal of medical informatics
PMID: 39889672

Abstract

BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique vulnerabilities and etiologies. However, existing approaches fail to fully utilize regional heterogeneous electronic health record (EHR) data. Integrating this data can provide a more comprehensive understanding of depression and anxiety in T2DM patients, leading to more personalized treatment strategies.

Authors

  • Wei Feng
    Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You'anmenwai, Xitoutiao No.10, Beijing, P. R. China.
  • Honghan Wu
    University College London, London, United Kingdom.
  • Hui Ma
    National Centre for Sensor Research and School of Biotechnology, Dublin City University, Collins Avenue, D09 Y5N0, 9 Dublin, Ireland.
  • Yuechuchu Yin
    Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China; Department of Information, the First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Zhenhuan Tao
    Nanjing Health Information Center, Nanjing, Jiangsu, China.
  • Shan Lu
    The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Yun Yu
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China. yuyun@njmu.edu.cn.
  • Cheng Wan
    School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
  • Yun Liu
    Google Health, Palo Alto, CA USA.

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