世界贸易中心的反应者用他们自己的话：用基于AI的访谈语言分析预测PTSD症状轨迹
World Trade Center responders in their own words: Predicting PTSD symptom trajectories with AI-based language analyses of interviews
摘要：
背景：9/11事件中对世界贸易中心（WTC）袭击的响应者的口述历史提供了关于痛苦和恢复力的丰富叙述。人工智能（AI）模型有望用自然语言检测精神病理学，但它们主要是在使用社交媒体的非临床环境中进行评估的。这项研究试图测试基于人工智能的语言评估预测响应者中PTSD症状轨迹的能力。研究方法：参与者是124名响应者，他们的健康状况在石溪世贸中心健康与健康计划中得到监测，他们完成了关于他们最初世贸中心经历的口述历史采访。PTSD症状的严重程度纵向测量PTSD检查表（PCL）长达7年的采访后。基于人工智能的指标计算抑郁，焦虑，神经质，外向以及基于字典的语言和人际风格的措施。线性回归和多水平模型估计AI指标与并发和随后的PTSD症状严重程度的相关性（通过错误发现率调整的显著性）。结果如下：横截面，更大的抑郁语言（β =0.32; p=0.043）和第一人称单数使用（β =0.31; p=0.044）与症状严重程度增加。纵向上，焦虑的语言预测未来PCL评分的恶化（beta=0.31; p=0.031），而第一人称复数使用（beta=-0.37; p=0.007）和较长的单词使用（beta=-0.36; p=0.007）预测改善。结论：这是第一项证明AI在理解弱势人群PTSD中的价值的研究。未来的研究应将这一应用扩展到其他创伤暴露和其他人口群体，特别是代表性不足的少数民族。
英文摘要：
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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