Detecting Preclinical PTSD Through Conversational AI: A Data-Driven Approach to Early Trauma Screening

This project tested whether machine learning can spot signs of post-traumatic stress disorder (PTSD) in mental health conversations, as a possible early screening tool. A Random Forest classifier was trained on 924 labeled Reddit posts from the r/PTSD community. Its features included emotional intensity across five areas (anxiety, depression, loneliness, positive affect and anger), sentiment, structural features such as word count and first-person references, and TF-IDF text features. The model reached 62.7% accuracy (AUC-ROC 0.667) with balanced precision and recall. Applied to two other datasets, it flagged 126 of 3,512 chatbot conversations (3.6%) and 69 of 2,775 counseling exchanges (2.5%) as showing PTSD indicators. Both rates are close to the general-population PTSD prevalence of about 3.5%. Detection was highest in conversations about domestic violence (23.8%). One notable finding is that structural features predicted PTSD better than emotional keywords, suggesting PTSD shows up in how people tell their stories as much as in the words they use. The authors conclude that AI-based detection is feasible as a supplementary triage tool for professional evaluation, not a replacement for it. They note three limitations: a modest training sample, possible bias in the Reddit-based labels, and no clinical validation.