  Background: Oral histories from 9/11 responders to the World Trade Center
(WTC) attacks provide rich narratives about distress and resilience. Artificial
Intelligence (AI) models promise to detect psychopathology in natural language,
but they have been evaluated primarily in non-clinical settings using social
media. This study sought to test the ability of AI-based language assessments
to predict PTSD symptom trajectories among responders. Methods: Participants
were 124 responders whose health was monitored at the Stony Brook WTC Health
and Wellness Program who completed oral history interviews about their initial
WTC experiences. PTSD symptom severity was measured longitudinally using the
PTSD Checklist (PCL) for up to 7 years post-interview. AI-based indicators were
computed for depression, anxiety, neuroticism, and extraversion along with
dictionary-based measures of linguistic and interpersonal style. Linear
regression and multilevel models estimated associations of AI indicators with
concurrent and subsequent PTSD symptom severity (significance adjusted by false
discovery rate). Results: Cross-sectionally, greater depressive language
(beta=0.32; p=0.043) and first-person singular usage (beta=0.31; p=0.044) were
associated with increased symptom severity. Longitudinally, anxious language
predicted future worsening in PCL scores (beta=0.31; p=0.031), whereas
first-person plural usage (beta=-0.37; p=0.007) and longer words usage
(beta=-0.36; p=0.007) predicted improvement. Conclusions: This is the first
study to demonstrate the value of AI in understanding PTSD in a vulnerable
population. Future studies should extend this application to other trauma
exposures and to other demographic groups, especially under-represented
minorities.
