World Trade Center responders in their own words: Predicting PTSD symptom trajectories with AI-based language analyses of interviews

• URL: http://arxiv.org/abs/2011.06457v1
• Date: Thu, 12 Nov 2020 15:57:23 GMT
• Title: World Trade Center responders in their own words: Predicting PTSD symptom trajectories with AI-based language analyses of interviews
• Authors: Youngseo Son, Sean A. P. Clouston, Roman Kotov, Johannes C. Eichstaedt, Evelyn J. Bromet, Benjamin J. Luft, and H Andrew Schwartz
• Abstract summary: This study tested the ability of AI-based language assessments to predict PTSD symptom trajectories among responders. Cross-sectionally, greater depressive language (beta=0.32; p43) and first-person singular usage (beta=0.31; p44) were associated with increased symptom severity. Longer words usage (beta=-0.36; p7) and longer words usage (beta=-0.36; p7) predicted improvement.
• Score: 6.700088567524812
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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