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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Psychopathology
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1439720
This article is part of the Research Topic Integrating Multimodal Approaches to Unravel Neural Mechanisms of Learning and Cognition View all 3 articles

Automatic Screening for Posttraumatic Stress Disorder (PTSD) in Early Adolescents Following the Ya'an Earthquake Using Text Mining Techniques

Provisionally accepted
Yuzhuo Yuan Yuzhuo Yuan 1,2Zhiyuan Liu Zhiyuan Liu 1,3Xuetao Tian Xuetao Tian 1,3*
  • 1 Beijing Normal University, Beijing, China
  • 2 Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, Beijing, China
  • 3 Faculty of Psychology, Beijing Normal University, Beijing, Beijing, China

The final, formatted version of the article will be published soon.

    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 selfnarratives 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 selfnarratives. The results showed that 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.

    Keywords: posttraumatic stress disorder, Automatic screening, text mining, Self-narratives, Natural Language Processing

    Received: 26 Jun 2024; Accepted: 25 Nov 2024.

    Copyright: © 2024 Yuan, Liu and Tian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Xuetao Tian, Beijing Normal University, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.