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 ( = 87) were aimed at predicting anxiety, while the remainder ( = 41) focused on posttraumatic stress disorder. They were mostly published since 2019 in computer science journals and tested algorithms using text ( = 72) as opposed to audio or video. Studies focused mainly on general populations ( = 92) and less on laboratory experiments ( = 23) or clinical populations ( = 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.

中文翻译：

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使用基于文本、音频和视频数据的机器学习算法预测一般和临床人群的焦虑和创伤后应激：系统评价


对使用自然行为（即文本、音频和视频数据）的机器学习 （ML） 算法的研究表明，这些技术可能有助于心理学和精神病学的个性化。然而，缺乏对当前技术水平的系统评价。此外，个别研究通常针对 ML 专家，他们可能会忽视其研究结果的潜在临床意义。在心理健康专业人士可以理解的叙述中，我们介绍了在 5 个心理学和 2 个计算机科学数据库中进行的系统评价。我们纳入了 128 项研究，这些研究评估了使用文本、音频和/或视频数据预测 ML 算法在预测焦虑和创伤后应激障碍方面的预测能力。大多数研究 （ = 87） 旨在预测焦虑，而其余研究 （ = 41） 则侧重于创伤后应激障碍。自 2019 年以来，它们主要发表在计算机科学期刊上，并使用文本 （ = 72） 而不是音频或视频测试算法。研究主要集中在一般人群 （ = 92） 上，较少关注实验室实验 （ = 23） 或临床人群 （ = 13）。方法学质量各不相同，报告的预测能力指标也各不相同，阻碍了研究之间的比较。三分之二的研究侧重于这两种疾病，报告了可接受或非常好的预测能力（仅包括高质量的研究）。33 项研究的结果无法解释，主要是由于信息缺失。对使用自然行为的 ML 算法的研究尚处于起步阶段，但如果方法标准化、结果报告和临床人群的研究得到改进，未来有可能为精神障碍（如焦虑和创伤后应激障碍）的诊断做出贡献。