{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Son Y"],"funding":["NIA NIH HHS","NIAAA NIH HHS"],"pagination":["918-926"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8692489"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["53(3)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>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"],"journal":["Psychological medicine"],"pubmed_title":["World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews."],"pmcid":["PMC8692489"],"funding_grant_id":["R01 AG049953","R01 AA028032"],"pubmed_authors":["Luft BJ","Schwartz HA","Clouston SAP","Bromet EJ","Eichstaedt JC","Kotov R","Son Y"],"additional_accession":[]},"is_claimable":false,"name":"World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews.","description":"<h4>Background</h4>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.<h4>Methods</h4>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","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-06-03T18:38:21.09Z","creation":"2025-04-06T21:54:05.847Z"},"accession":"S-EPMC8692489","cross_references":{"pubmed":["34154682"],"doi":["10.1017/S0033291721002294"]}}