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Zuckerman STEM Leadership Program","award":["Project Title: Assessing Risk Factors for Childbirth-Related Post-Traumatic Stress Disorder"],"award-info":[{"award-number":["Project Title: Assessing Risk Factors for Childbirth-Related Post-Traumatic Stress Disorder"]}]},{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["R01HD108619"],"award-info":[{"award-number":["R01HD108619"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats: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 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\u2019s and ADA\u2019s 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.<\/jats:p>","DOI":"10.1038\/s41598-024-54242-2","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T06:01:52Z","timestamp":1712815312000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["AI and narrative embeddings detect PTSD following childbirth via birth stories"],"prefix":"10.1038","volume":"14","author":[{"given":"Alon","family":"Bartal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathleen M.","family":"Jagodnik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabrina J.","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharon","family":"Dekel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,11]]},"reference":[{"issue":"12","key":"54242_CR1","doi-asserted-by":"publisher","first-page":"e38161","DOI":"10.2196\/38161","volume":"10","author":"L Wang","year":"2022","unstructured":"Wang, L. et al. 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