Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models
Not on the map. Why?
Abstract (arXiv)
Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify patients. This study evaluates natural language processing approaches for detecting PTSD from clinical interview transcripts. We compared general and mental health-specific transformer models (BERT/RoBERTa), embedding-based methods (SentenceBERT/LLaMA), and large language model prompting strategies (zero-shot/few-shot/chain-of-thought) using the DAIC-WOZ dataset. Domain-specific end-to-end models significantly outperformed general models (Mental-RoBERTa AUPRC=0.675+/-0.084 vs. RoBERTa-base 0.599+/-0.145). SentenceBERT embeddings with neural networks achieved the highest overall performance (AUPRC=0.758+/-0.128). Few-shot prompting using DSM-5 criteria yielded competitive results with two examples (AUPRC=0.737). Performance varied significantly across symptom severity and comorbidity status with depression, with higher accuracy for severe PTSD cases and patients with comorbid depression. Our findings highlight the potential of domain-adapted embeddings and LLMs for scalable screening while underscoring the need for improved detection of nuanced presentations and offering insights for developing clinically viable AI tools for PTSD assessment.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 2 of its 5 references that are in the map. First posted on arXiv 1 Apr 2025.
Builds on
2 of 5 references.
References · 2
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Can LLMs Infer Personality from Real World Conversations?cited by 4
- 2025SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregationcited by 1
- 2026Depression Detection at the Point of Care: Automated Analysis of Linguistic Signals from Routine Primary Care Encounterscited by 0
- 2026Depression Detection at the Point of Care: Automated Analysis of Linguistic Signals from Routine Primary Care Encounters11 Mar 2026
- 2025Can LLMs Infer Personality from Real World Conversations?18 Jul 2025
- 2025SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation7 May 2025
Similar, not cited
Close in wording, no citation either way
- 2026A Systematic Evaluation of Large Language Models for PTSD Severity Estimation: The Role of Contextual Knowledge and Modeling Strategies
- 2025Evaluating Large Language Models for Anxiety, Depression, and Stress Detection: Insights into Prompting Strategies and Synthetic Data
- 2024Aligning Large Language Models for Enhancing Psychiatric Interviews Through Symptom Delineation and Summarization: Pilot Study
- 2023Towards Interpretable Mental Health Analysis with Large Language Models
- 2025Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
- 2023An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
- 2025A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG
- 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data