Yuan Yuzhuo, Liu Zhiyuan, Miao Wei, Tian Xuetao
Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
Faculty of Psychology, Beijing Normal University, Beijing, China.
Front Psychiatry. 2024 Dec 11;15:1439720. doi: 10.3389/fpsyt.2024.1439720. eCollection 2024.
Self-narratives about traumatic experiences and symptoms are informative for early identification of potential patients; however, their use in clinical screening is limited. This study aimed to develop an automated screening method that analyzes self-narratives of early adolescent earthquake survivors to screen for PTSD in a timely and effective manner.
An inquiry-based questionnaire consisting of a series of open-ended questions about trauma history and psychological symptoms, was designed to simulate the clinical structured interviews based on the DSM-5 diagnostic criteria, and was used to collect self-narratives from 430 survivors who experienced the Ya'an earthquake in Sichuan Province, China. Meanwhile, participants completed the PTSD Checklist for DSM-5 (PCL-5). Text classification models were constructed using three supervised learning algorithms (BERT, SVM, and KNN) to identify PTSD symptoms and their corresponding behavioral indicators in each sentence of the self-narratives.
The prediction accuracy for symptom-level classification reached 73.2%, and 67.2% for behavioral indicator classification, with the BERT performing the best.
These findings demonstrate that self-narratives combined with text mining techniques provide a promising approach for automated, rapid, and accurate PTSD screening. Moreover, by conducting screenings in community and school settings, this approach equips clinicians and psychiatrists with evidence of PTSD symptoms and associated behavioral indicators, improving the effectiveness of early detection and treatment planning.
关于创伤经历和症状的自我叙述有助于早期识别潜在患者；然而，它们在临床筛查中的应用有限。本研究旨在开发一种自动筛查方法，分析青少年地震幸存者的自我叙述，以便及时、有效地筛查创伤后应激障碍（PTSD）。
设计了一份基于询问的问卷，包含一系列关于创伤史和心理症状的开放式问题，旨在模拟基于《精神疾病诊断与统计手册》第5版（DSM-5）诊断标准的临床结构化访谈，并用于收集430名经历中国四川省雅安地震的幸存者的自我叙述。同时，参与者完成了DSM-5的创伤后应激障碍检查表（PCL-5）。使用三种监督学习算法（BERT、支持向量机和K近邻算法）构建文本分类模型，以识别自我叙述中每句话中的PTSD症状及其相应的行为指标。
症状水平分类的预测准确率达到73.2%，行为指标分类的预测准确率为67.2%，其中BERT表现最佳。
这些发现表明，自我叙述与文本挖掘技术相结合为自动、快速、准确地筛查PTSD提供了一种有前景的方法。此外，通过在社区和学校环境中进行筛查，这种方法为临床医生和精神科医生提供了PTSD症状及相关行为指标的证据，提高了早期检测和治疗规划的有效性。
