Use of Machine-Learning Algorithms Based on Text, Audio and Video Data in the Prediction of Anxiety and Post-Traumatic Stress in General and Clinical Populations: A Systematic Review
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In: Biological Psychiatry, Vol. 96, No. 7, 01.10.2024, p. 519-531.
Research output: Journal contributions › Scientific review articles › Research
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TY - JOUR
T1 - Use of Machine-Learning Algorithms Based on Text, Audio and Video Data in the Prediction of Anxiety and Post-Traumatic Stress in General and Clinical Populations
T2 - A Systematic Review
AU - Ciharova, Marketa
AU - Amarti, Khadicha
AU - van Breda, Ward
AU - Peng, Xianhua
AU - Lorente-Català, Rosa
AU - Funk, Burkhardt
AU - Hoogendoorn, Mark
AU - Koutsouleris, Nikolaos
AU - Fusar-Poli, Paolo
AU - Karyotaki, Eirini
AU - Cuijpers, Pim
AU - Riper, Heleen
N1 - Publisher Copyright: © 2024 Society of Biological Psychiatry
PY - 2024/10/1
Y1 - 2024/10/1
N2 - 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.
AB - 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.
KW - Informatics
KW - Anxiety
KW - Audio
KW - Machine Learning
KW - Posttraumatic stress
KW - Text
KW - Video
UR - http://www.scopus.com/inward/record.url?scp=85199080534&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/9a11e3ab-e4a0-3289-8084-a5065ef7f98c/
U2 - 10.1016/j.biopsych.2024.06.002
DO - 10.1016/j.biopsych.2024.06.002
M3 - Scientific review articles
C2 - 38866173
VL - 96
SP - 519
EP - 531
JO - Biological Psychiatry
JF - Biological Psychiatry
SN - 0006-3223
IS - 7
ER -