Computer-Based PTSD Assessment in VR Exposure Therapy
Computer-Based PTSD Assessment in VR Exposure Therapy
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DOI：
10.1007/978-3-030-59990-4_32
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Leili Tavabi;Anna Poon;A. Rizzo;M. Soleymani
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文献类型：
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作者：
Leili Tavabi;Anna Poon;A. Rizzo;M. Soleymani
创伤后应激障碍（PTSD）是一种影响经历创伤事件的人的心理健康状况。除了PTSD的临床诊断标准外，还可能发生声音，语言，面部表情和头部运动的行为变化。在本文中，我们展示了如何使用在具有自我报告的PTSD评分的一般人群上训练的机器学习模型来提供行为指标，从而提高患者临床诊断的准确性。这两个数据集都是从虚拟代理（SimSensei）进行的临床访谈中收集的[10]。这些临床数据来自PTSD患者，他们是性侵犯的受害者，正在接受VR暴露疗法。根据自我报告的PCL-C评分，对语言，视觉和声音特征进行递归神经网络训练，以识别PTSD [4]。然后，我们进行决策融合，融合三种模式，以识别临床诊断为PTSD的患者，实现F1评分为0.85。我们的分析表明，基于机器的创伤后应激障碍评估与自我报告的创伤后应激障碍评分可以推广到不同的群体，并部署到帮助诊断创伤后应激障碍。
Post-traumatic stress disorder (PTSD) is a mental health condition affecting people who experienced a traumatic event. In addition to the clinical diagnostic criteria for PTSD, behavioral changes in voice, language, facial expression and head movement may occur. In this paper, we demonstrate how a machine learning model trained on a general population with self-reported PTSD scores can be used to provide behavioral metrics that could enhance the accuracy of the clinical diagnosis with patients. Both datasets were collected from a clinical interview conducted by a virtual agent (SimSensei) [10]. The clinical data was recorded from PTSD patients, who were victims of sexual assault, undergoing a VR exposure therapy. A recurrent neural network was trained on verbal, visual and vocal features to recognize PTSD, according to self-reported PCL-C scores [4]. We then performed decision fusion to fuse three modalities to recognize PTSD in patients with a clinical diagnosis, achieving an F1-score of 0.85. Our analysis demonstrates that machine-based PTSD assessment with self-reported PTSD scores can generalize across different groups and be deployed to assist diagnosis of PTSD.