Cargando…
World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews
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 us...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8692489/ https://www.ncbi.nlm.nih.gov/pubmed/34154682 http://dx.doi.org/10.1017/S0033291721002294 |
_version_ | 1784618957902184448 |
---|---|
author | Son, Youngseo Clouston, Sean A. P. Kotov, Roman Eichstaedt, Johannes C. Bromet, Evelyn J. Luft, Benjamin J. Schwartz, H. Andrew |
author_facet | Son, Youngseo Clouston, Sean A. P. Kotov, Roman Eichstaedt, Johannes C. Bromet, Evelyn J. Luft, Benjamin J. Schwartz, H. Andrew |
author_sort | Son, Youngseo |
collection | PubMed |
description | 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 (β = 0.32; p = 0.049) and first-person singular usage (β = 0.31; p = 0.049) were associated with increased symptom severity. Longitudinally, anxious language predicted future worsening in PCL scores (β = 0.30; p = 0.049), whereas first-person plural usage (β = −0.36; p = 0.014) and longer words usage (β = −0.35; p = 0.014) 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. |
format | Online Article Text |
id | pubmed-8692489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86924892022-12-22 World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews Son, Youngseo Clouston, Sean A. P. Kotov, Roman Eichstaedt, Johannes C. Bromet, Evelyn J. Luft, Benjamin J. Schwartz, H. Andrew Psychol Med Original Article 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 (β = 0.32; p = 0.049) and first-person singular usage (β = 0.31; p = 0.049) were associated with increased symptom severity. Longitudinally, anxious language predicted future worsening in PCL scores (β = 0.30; p = 0.049), whereas first-person plural usage (β = −0.36; p = 0.014) and longer words usage (β = −0.35; p = 0.014) 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. Cambridge University Press 2023-02 2021-06-22 /pmc/articles/PMC8692489/ /pubmed/34154682 http://dx.doi.org/10.1017/S0033291721002294 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
spellingShingle | Original Article Son, Youngseo Clouston, Sean A. P. Kotov, Roman Eichstaedt, Johannes C. Bromet, Evelyn J. Luft, Benjamin J. Schwartz, H. Andrew World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews |
title | World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews |
title_full | World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews |
title_fullStr | World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews |
title_full_unstemmed | World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews |
title_short | World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews |
title_sort | world trade center responders in their own words: predicting ptsd symptom trajectories with ai-based language analyses of interviews |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8692489/ https://www.ncbi.nlm.nih.gov/pubmed/34154682 http://dx.doi.org/10.1017/S0033291721002294 |
work_keys_str_mv | AT sonyoungseo worldtradecenterrespondersintheirownwordspredictingptsdsymptomtrajectorieswithaibasedlanguageanalysesofinterviews AT cloustonseanap worldtradecenterrespondersintheirownwordspredictingptsdsymptomtrajectorieswithaibasedlanguageanalysesofinterviews AT kotovroman worldtradecenterrespondersintheirownwordspredictingptsdsymptomtrajectorieswithaibasedlanguageanalysesofinterviews AT eichstaedtjohannesc worldtradecenterrespondersintheirownwordspredictingptsdsymptomtrajectorieswithaibasedlanguageanalysesofinterviews AT brometevelynj worldtradecenterrespondersintheirownwordspredictingptsdsymptomtrajectorieswithaibasedlanguageanalysesofinterviews AT luftbenjaminj worldtradecenterrespondersintheirownwordspredictingptsdsymptomtrajectorieswithaibasedlanguageanalysesofinterviews AT schwartzhandrew worldtradecenterrespondersintheirownwordspredictingptsdsymptomtrajectorieswithaibasedlanguageanalysesofinterviews |