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

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

IntroductionMachine 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 sessi...

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Bibliographic Details
Main Authors: Jin-Hyun Park, Yu-Bin Shin, Dooyoung Jung, Ji-Won Hur, Seung Pil Pack, Heon-Jeong Lee, Hwamin Lee, Chul-Hyun Cho
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Psychiatry
Subjects:
machine learning
multimodal data
digital phenotyping
digital psychiatry
social anxiety disorder
virtual reality intervention
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1504190/full
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