Sawalha Jeff, Yousefnezhad Muhammad, Shah Zehra, Brown Matthew R G, Greenshaw Andrew J, Greiner Russell
Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.
Department of Computer Science, University of Alberta, Edmonton, AB, Canada.
Front Psychiatry. 2022 Feb 1;12:811392. doi: 10.3389/fpsyt.2021.811392. eCollection 2021.
Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.
由于新冠疫情，创伤后应激障碍（PTSD）的发病率显著上升。远程医疗已成为监测此类疾病症状的一种手段。部分原因是疫情导致治疗干预的隔离或难以获得。可能需要额外的筛查工具，以通过虚拟媒介加强对PTSD的识别和诊断。情感分析是指使用自然语言处理（NLP）从文本信息中提取情感内容。在我们的研究中，我们在文本数据上训练一个机器学习（ML）模型，该文本数据是音频/视觉情感挑战与研讨会（AVEC - 19）语料库的一部分，通过对半结构化访谈进行情感分析来识别患有PTSD的个体。我们的样本量包括188名无PTSD的个体和87名患有PTSD的个体。访谈由一个虚拟角色（艾莉）通过视频会议进行。我们的模型在用于AVEC - 19挑战的一个留出数据集上能够达到80.4%的平衡准确率。此外，我们实施了各种划分技术来确定我们的模型是否具有足够的通用性。这表明学习到的模型可以使用语音情感分析来识别PTSD的存在，即使是通过虚拟媒介。这可以作为在新冠疫情期间检测心理健康异常的一种重要、易于获得且成本低廉的工具。
