Department of Psychology, University of Turin, Via Verdi 10, Turin 10124, Italy.
Department of Psychiatry, Amsterdam Public Health, University of Amsterdam, Amsterdam UMC location, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands.
Psychiatry Res. 2022 Oct;316:114753. doi: 10.1016/j.psychres.2022.114753. Epub 2022 Jul 28.
Previous studies showed that textual information could be used to screen respondents for posttraumatic stress disorder (PTSD). In this study, we explored the feasibility of using language features extracted from short text descriptions respondents provided of stressful events to predict trauma-related symptoms assessed using the Global Psychotrauma Screen. Texts were analyzed with both closed- and open-vocabulary methods to extract language features representing the occurrence of words, phrases, or specific topics in the description of stressful events. We also evaluated whether combining language features with self-report information, including respondents' demographics, event characteristics, and risk factors for trauma-related disorders, would improve the prediction performance. Data were collected using an online survey on a cross-national sample of 5048 respondents. Results showed that language data achieved the highest predictive power when both closed- and open-vocabulary features were included as predictors. Combining language data and self-report information resulted in a significant increase in performance and in a model which achieved good accuracy as a screener for probable PTSD diagnosis (.7 < AUC ≤ .8), with similar results regardless of the length of the text description of the event. Overall, results indicated that short texts add to the detection of trauma-related symptoms and probable PTSD diagnosis.
先前的研究表明，文本信息可用于筛选创伤后应激障碍（PTSD）患者。在这项研究中，我们探索了使用从受访者对压力事件的简短描述中提取的语言特征来预测使用全球心理创伤筛查（Global Psychotrauma Screen）评估的与创伤相关的症状的可行性。使用封闭词汇和开放词汇方法分析文本，以提取语言特征，这些特征代表压力事件描述中单词、短语或特定主题的出现。我们还评估了将语言特征与自我报告信息（包括受访者的人口统计学信息、事件特征和与创伤相关障碍的风险因素）相结合是否会提高预测性能。数据是通过对来自 5048 名受访者的跨国样本的在线调查收集的。结果表明，当将封闭词汇和开放词汇特征都作为预测因素时，语言数据的预测能力最高。将语言数据和自我报告信息相结合，显著提高了性能，使作为可能的 PTSD 诊断筛查器的模型具有良好的准确性（0.7 <AUC ≤ 0.8），无论事件描述的文本长度如何，结果都相似。总体而言，结果表明，短文本可帮助检测与创伤相关的症状和可能的 PTSD 诊断。
