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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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1504190/full |
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| author | Jin-Hyun Park Yu-Bin Shin Dooyoung Jung Ji-Won Hur Seung Pil Pack Heon-Jeong Lee Hwamin Lee Chul-Hyun Cho Chul-Hyun Cho |
| author_facet | Jin-Hyun Park Yu-Bin Shin Dooyoung Jung Ji-Won Hur Seung Pil Pack Heon-Jeong Lee Hwamin Lee Chul-Hyun Cho Chul-Hyun Cho |
| author_sort | Jin-Hyun Park |
| collection | DOAJ |
| description | 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 sessions for social anxiety disorder (SAD) patients and to evaluate their predictive performance across each data type.MethodsThis study included 32 SAD-diagnosed individuals, and finalized a dataset of 132 samples from 25 participants. It utilized multimodal (physiological and acoustic) data from VR sessions to simulate social anxiety scenarios. This study employed extended Geneva minimalistic acoustic parameter set for acoustic feature extraction and extracted statistical attributes from time series-based physiological responses. We developed ML models that predict the upper tertile group for various anxiety symptoms in SAD using Random Forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) models. The best parameters were explored through grid search or random search, and the models were validated using stratified cross-validation and leave-one-out cross-validation.ResultsThe CatBoost, using multimodal features, exhibited high performance, particularly for the Social Phobia Scale with an area under the receiver operating characteristics curve (AUROC) of 0.852. It also showed strong performance in predicting cognitive symptoms, with the highest AUROC of 0.866 for the Post-Event Rumination Scale. For generalized anxiety, the LightGBM’s prediction for the State-Trait Anxiety Inventory-trait led to an AUROC of 0.819. In the same analysis, models using only physiological features had AUROCs of 0.626, 0.744, and 0.671, whereas models using only acoustic features had AUROCs of 0.788, 0.823, and 0.754.ConclusionsThis study showed that a ML algorithm using integrated multimodal data can predict upper tertile anxiety symptoms in patients with SAD with higher performance than acoustic or physiological data obtained during a VR session. The results of this study can be used as evidence for personalized VR sessions and to demonstrate the strength of the clinical use of multimodal data. |
| format | Article |
| id | doaj-art-abe8544484544c49b63ce4626299801a |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Psychiatry |
| spelling | doaj-art-abe8544484544c49b63ce4626299801a2025-01-31T10:10:39ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-01-011510.3389/fpsyt.2024.15041901504190Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessionsJin-Hyun Park0Yu-Bin Shin1Dooyoung Jung2Ji-Won Hur3Seung Pil Pack4Heon-Jeong Lee5Hwamin Lee6Chul-Hyun Cho7Chul-Hyun Cho8Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of KoreaDepartment of Psychiatry, Korea University College of Medicine, Seoul, Republic of KoreaGraduate School of Health Science and Technology, Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of KoreaSchool of Psychiatry, Korea University, Seoul, Republic of KoreaDepartment of Biotechnology and Bioinformatics, Korea University, Sejong, Republic of KoreaDepartment of Psychiatry, Korea University College of Medicine, Seoul, Republic of KoreaDepartment of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of KoreaDepartment of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of KoreaDepartment of Psychiatry, Korea University College of Medicine, Seoul, Republic of KoreaIntroductionMachine 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.MethodsThis study included 32 SAD-diagnosed individuals, and finalized a dataset of 132 samples from 25 participants. It utilized multimodal (physiological and acoustic) data from VR sessions to simulate social anxiety scenarios. This study employed extended Geneva minimalistic acoustic parameter set for acoustic feature extraction and extracted statistical attributes from time series-based physiological responses. We developed ML models that predict the upper tertile group for various anxiety symptoms in SAD using Random Forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) models. The best parameters were explored through grid search or random search, and the models were validated using stratified cross-validation and leave-one-out cross-validation.ResultsThe CatBoost, using multimodal features, exhibited high performance, particularly for the Social Phobia Scale with an area under the receiver operating characteristics curve (AUROC) of 0.852. It also showed strong performance in predicting cognitive symptoms, with the highest AUROC of 0.866 for the Post-Event Rumination Scale. For generalized anxiety, the LightGBM’s prediction for the State-Trait Anxiety Inventory-trait led to an AUROC of 0.819. In the same analysis, models using only physiological features had AUROCs of 0.626, 0.744, and 0.671, whereas models using only acoustic features had AUROCs of 0.788, 0.823, and 0.754.ConclusionsThis study showed that a ML algorithm using integrated multimodal data can predict upper tertile anxiety symptoms in patients with SAD with higher performance than acoustic or physiological data obtained during a VR session. The results of this study can be used as evidence for personalized VR sessions and to demonstrate the strength of the clinical use of multimodal data.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1504190/fullmachine learningmultimodal datadigital phenotypingdigital psychiatrysocial anxiety disordervirtual reality intervention |
| spellingShingle | Jin-Hyun Park Yu-Bin Shin Dooyoung Jung Ji-Won Hur Seung Pil Pack Heon-Jeong Lee Hwamin Lee Chul-Hyun Cho Chul-Hyun Cho Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions Frontiers in Psychiatry machine learning multimodal data digital phenotyping digital psychiatry social anxiety disorder virtual reality intervention |
| title | Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions |
| title_full | Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions |
| title_fullStr | Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions |
| title_full_unstemmed | Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions |
| title_short | Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions |
| title_sort | machine learning prediction of anxiety symptoms in social anxiety disorder utilizing multimodal data from virtual reality sessions |
| topic | machine learning multimodal data digital phenotyping digital psychiatry social anxiety disorder virtual reality intervention |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1504190/full |
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