Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions.

Journal: Frontiers in psychiatry
Published Date: Jan 7, 2025

Abstract

INTRODUCTION: Machine learning (ML) is an effective tool for predicting mental states and is a key technology in digital psychiatry. This study aimed to develop ML algorithms to predict the upper tertile group of various anxiety symptoms based on multimodal data from virtual reality (VR) therapy sessions for social anxiety disorder (SAD) patients and to evaluate their predictive performance across each data type.

Authors

  • Jin-Hyun Park
    Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
  • Yu-Bin Shin
    Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.
  • Dooyoung Jung
    Graduate School of Health Science and Technology, Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea.
  • Ji-Won Hur
    School of Psychiatry, Korea University, Seoul, Republic of Korea.
  • Seung Pil Pack
    Department of Biotechnology and Bioinformatics, Korea University, Sejong, Republic of Korea.
  • Heon-Jeong Lee
    Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.
  • Hwamin Lee
    Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
  • Chul-Hyun Cho
    Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.

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