Can machine learning predict PTSD symptoms from trauma narratives of children and adolescents?

Background

Machine learning approaches are being increasingly tested as a potential means of identifying mental health conditions. Narrative features of trauma memories are proposed to play a significant role in the development of post-traumatic stress disorder (PTSD), meaning that trauma narratives provide an excellent context in which to test machine learning capabilities. The potential for children's trauma narratives to predict post-traumatic stress remains particularly poorly studied. Here, we tested whether the application of machine learning to trauma narrative characteristics can predict PTSD symptoms in young individuals exposed to trauma.Study methodology: Two pre-trained large language models and two benchmark models were fine-tuned and trained to predict PTSD symptom severity from children's autobiographical narratives of a traumatic event. Data comprised narratives collected one month post-trauma from 400 individuals aged 7-17 years old who experienced a psychological trauma that led to attendance at emergency departments in the United Kingdom (N = 178) and South Africa (N = 222), as well as self-reported PTSD symptoms and trauma memory features.Findings: Both pre-trained and benchmark models demonstrated poor predictive performance across trauma narratives in the United Kingdom, South Africa, and the combined datasets (e.g. RoBERTa R²  =  -.05; LASSO R² ≈ 0). However, adding self-reported trauma memory features, disorganisation, and sensory vividness improved the benchmark models' performances, especially in the UK dataset (e.g. LASSO R² = .57; XGBoost R² = .45).

Conclusions

These findings indicate that while trauma narratives alone offer limited predictive value, incorporating self-reported trauma memory characteristics substantially enhances model performance, highlighting the importance of focusing on subjective reports to develop scalable automated tools for PTSD risk prediction in youth.