Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: The prediction accuracy for symptom-level classification reached 73.2%, and 67.2% for behavioral indicator classification, with the BERT performing the best. CONCLUSIONS: 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.