Academic Journal
Use of Machine Learning Algorithms Based on Text, Audio, and Video Data in the Prediction of Anxiety and Posttraumatic Stress in General and Clinical Populations: A Systematic Review
| العنوان: | Use of Machine Learning Algorithms Based on Text, Audio, and Video Data in the Prediction of Anxiety and Posttraumatic Stress in General and Clinical Populations: A Systematic Review |
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| المؤلفون: | Ciharova, Marketa, Amarti, Khadicha, Peng, Xianhua, Karyotaki, Eirini, Cuijpers, Pim, Riper, Heleen, van Breda, Ward, Hoogendoorn, Mark, Lorente Català, Rosa, Funk, Burkhardt, Koutsouleris, Nikolaos |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2024 |
| المجموعة: | Repositori Universitat Jaume I (Repositorio UJI) |
| مصطلحات موضوعية: | anxiety, audio, machine learning, posttraumatic stress, text, video |
| الوصف: | Research in machine learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state of the art is missing. Moreover, individual studies often target ML experts who may overlook potential clinical implications of their findings. In a narrative accessible to mental health professionals, we present a systematic review conducted in 5 psychology and 2 computer science databases. We included 128 studies that assessed the predictive power of ML algorithms using text, audio, and/or video data in the prediction of anxiety and posttraumatic stress disorder. Most studies (n = 87) were aimed at predicting anxiety, while the remainder (n = 41) focused on posttraumatic stress disorder. They were mostly published since 2019 in computer science journals and tested algorithms using text (n = 72) as opposed to audio or video. Studies focused mainly on general populations (n = 92) and less on laboratory experiments (n = 23) or clinical populations (n = 13). Methodological quality varied, as did reported metrics of the predictive power, hampering comparison across studies. Two-thirds of studies, which focused on both disorders, reported acceptable to very good predictive power (including high-quality studies only). The results of 33 studies were uninterpretable, mainly due to missing information. Research into ML algorithms using natural behavior is in its infancy but shows potential to contribute to diagnostics of mental disorders, such as anxiety and posttraumatic stress disorder, in the future if standardization of methods, reporting of results, and research in clinical populations are improved. ; This project was part of the IT4Anxiety project funded by INTERREG North-West Europe, European Union. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | 13 p.; application/pdf |
| اللغة: | English |
| Relation: | Biological psychiatry, 2024, vol. 96, no 7; https://www.sciencedirect.com/science/article/pii/S0006322324013623; https://doi.org/10.1016/j.biopsych.2024.06.002; http://hdl.handle.net/10234/703015 |
| DOI: | 10.1016/j.biopsych.2024.06.002 |
| الاتاحة: | http://hdl.handle.net/10234/703015 https://doi.org/10.1016/j.biopsych.2024.06.002 |
| Rights: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: | edsbas.A56E9B92 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.biopsych.2024.06.002 |
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