{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:01:39Z","timestamp":1772236899361,"version":"3.50.1"},"posted":{"date-parts":[[2024,2,26]]},"group-title":"In Review","reference-count":67,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,10,10]]},"abstract":"<title>Abstract<\/title>\n                <p>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 1,295 women who gave birth in the last six months and were 18\u2009+\u2009years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT\u2019s and ADA\u2019s potential to screen for CB-PTSD by analyzing maternal childbirth narratives only. 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: 0.82) ChatGPT and six previously published large language models (LLMs) 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. 1<\/p>","DOI":"10.21203\/rs.3.rs-3428787\/v2","type":"posted-content","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T11:30:06Z","timestamp":1708947006000},"source":"Crossref","is-referenced-by-count":1,"title":["ChatGPT Demonstrates Potential for Identifying Psychiatric Disorders: Application to Childbirth-Related Post-Traumatic Stress Disorder"],"prefix":"10.21203","author":[{"given":"Alon","family":"Bartal","sequence":"first","affiliation":[{"name":"Bar-Ilan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathleen M.","family":"Jagodnik","sequence":"additional","affiliation":[{"name":"Bar-Ilan University, Harvard Medical School & Massachusetts General Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabrina J.","family":"Chan","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharon","family":"Dekel","sequence":"additional","affiliation":[{"name":"Harvard Medical School & Massachusetts General Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"issue":"12","key":"ref1","doi-asserted-by":"crossref","DOI":"10.2196\/38161","article-title":"Boosting delirium identification accuracy with sentiment-based natural language processing: Mixed methods study","volume":"10","author":"Wang L","year":"2022","unstructured":"Wang, L. et al. 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