ArticleScientific reports2024
AI and narrative embeddings detect PTSD following childbirth via birth stories.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 5 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.Archives of women's mental health · 2026Pooled it
- Effectiveness of Chatbots in Mental Health Screening and Assessment: Systematic Review.JMIR mental health · 2026Pooled it
- Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.BMC psychiatry · 2026Pooled it
- Linguistic markers for identifying post-traumatic stress disorder and associated symptoms: a systematic literature review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Exploring the application boundaries of LLMs in mental health: a systematic scoping review.Frontiers in psychology · 2025Pooled it
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- Large Language Models for Mental Health Prediction: Scoping Review of Bias and Clinical Utility Documentation in 2019-2024.JMIR AI · 2026Review
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- Exploring the potential of ChatGPT as a digital advisor in acute psychiatric crises: a feasibility study.Der Nervenarzt · 2026Article
- The psychological impact of childbirth: Unscheduled cesarean delivery associates with increased risk for acute stress response.Pregnancy (Hoboken, N.J.) · 2026Article
- Article
- ChatGPT Clinical Use in Mental Health Care: Scoping Review of Empirical Evidence.JMIR mental health · 2025Article
- Can machine learning predict PTSD symptoms from trauma narratives of children and adolescents?European journal of psychotraumatology · 2025Article
- Practitioner Perspectives on the Uses of Generative AI Chatbots in Mental Health Care: Mixed Methods Study.JMIR human factors · 2025Article
- A paper-and-pencil questionnaire outperforms GPT for measuring cognitive vulnerability to depression and predicting depressive symptoms.Scientific reports · 2025Article
- The Applications of Large Language Models in Mental Health: Scoping Review.Journal of medical Internet research · 2025Article
- Large Language Models and Text Embeddings for Detecting Depression and Suicide in Patient Narratives.JAMA network open · 2025Article
- Balancing risks and benefits: clinicians' perspectives on the use of generative AI chatbots in mental healthcare.Frontiers in digital health · 2025Article
- Cognitive biases: potential behavioral marker for future development of postpartum depression and childbirth-related post-traumatic stress disorder.Frontiers in psychiatry · 2025Article
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4 authors.
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Abstract
Free-text analysis using machine learning (ML)-based natural language processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol. Using a sample of 1295 women who gave birth in the last six months and were 18+ years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT's and ADA's potential to screen for CB-PTSD by analyzing maternal childbirth narratives. The PTSD Checklist for DSM-5 (PCL-5; cutoff 31) was used to assess CB-PTSD. By developing an ML model that utilizes numerical vector representation of the ADA model, we identify CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.81) ChatGPT and six previously published large text-embedding models trained on mental health or clinical domains data, suggesting that the ADA model can be harnessed to identify CB-PTSD. Our modeling approach could be generalized to assess other mental health disorders.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.