# Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models

arXiv: https://arxiv.org/abs/2504.01216 · HTML: https://mlsanity.com/paper/2504.01216/

## 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 (most cited first, 2 of 2)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)

## Built on by (most cited first, 3 of 3)

- [Can LLMs Infer Personality from Real World Conversations?](https://mlsanity.com/paper/2507.14355/) (2025)
- [SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation](https://mlsanity.com/paper/2505.04152/) (2025)
- [Depression Detection at the Point of Care: Automated Analysis of Linguistic Signals from Routine Primary Care Encounters](https://mlsanity.com/paper/2604.06193/) (2026)

## 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.