@article{93a8dcd6830c40d2a3f63f74345487e5,
title = "Text mining to improve screening for trauma-related symptoms in a global sample",
abstract = "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{\textquoteright} 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.",
keywords = "PTSD, Screening, Text mining, Trauma-related symptoms",
author = "D. Marengo and Hoeboer, {C. M.} and Veldkamp, {B. P.} and {GPS-txt consortium} and M. Olff",
note = "Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Raw data were generated through the Global Collaboration on Traumatic Stress (GC-TS). Derived data supporting the findings of this study are available on the GC-TS website: www.global-psychotrauma.net. We thank all ambassadors who helped include participants around the world, in particular: Helene Aakvaag, Dean Ajdukovic, Zafer Altunbezel, Xenia Anastassiou-Hadjicharalambous, Vittoria Ardino, Anne Bakker, Sara Belquaid, Jonathon Bisson, Erine Br{\"o}cker, Lucia Cantoni, Ruby Charak, Marylene Cloitre, Aida Dias, Malgorzata Dragan, Atle Dyregrov, Julian Ford, Sarah Gallo, Paul Frewen, Wissam El Hage, Juanita Haagsma, Jackie June ter Heide, Danielle Hett, Maryke Hewett, Jana Javakhishvili, Nancy Kassam-Adams, Yoshiharu Kim, Christian Kristensen, Rachel Langevin, Juliana Lanza, Patrick Lorenz, Weili Lu, Brigitte Lueger-Schuster, Sam Manickam, Marcelo Mello, Gladys Mwiti, Natallia Nalyvaiko, Angela Nickerson, Misari Oe, Heval {\"O}zgen, Janaina Pinto, Daniela Rabellino, Luisa Sales, Carolina Salgado, Julia Schellong, Ulrich Schnyder, Soraya Seedat, Nadejda Semenova, Andrew Smith, Sjacko Sobczak, Erik de Soir, Zhonglin Tan, Keerthana Thatavarthi, Carmelo Vazquez, Anne Wagner, Li Wang, Irina Zrnic. Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2022",
month = oct,
day = "1",
doi = "https://doi.org/10.1016/j.psychres.2022.114753",
language = "English",
volume = "316",
journal = "Psychiatry Research",
issn = "0165-1781",
publisher = "Elsevier Ireland Ltd",
}