Interdisciplinary Approach To Identify Language Markers For Post-traumatic Stress Disorder Using Machine Learning And Deep Learning

This article discusses the potential use of language as a diagnostic biomarker for post-traumatic stress disorder (PTSD). A three-step interdisciplinary methodology is proposed, integrating expertise from psychiatry, linguistics, and Natural Language Processing (NLP) to examine the relationship between language and PTSD. Results show that language features can predict PTSD with comparable accuracy to a gold standard questionnaire. The study also controls for confounding factors and integrates automated methods with qualitative analysis.