Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives

Alon Bartal, Kathleen M. Jagodnik, Sabrina J. Chan, Mrithula S. Babu, Sharon Dekel

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


BACKGROUND: Maternal mental disorders are considered a leading complication of childbirth and a common contributor to maternal death. In addition to undermining maternal welfare, untreated postpartum psychopathology can result in child emotional and physical neglect and associated significant pediatric health costs. Some women may experience traumatic childbirth and develop posttraumatic stress disorder symptoms after delivery (childbirth-related posttraumatic stress disorder). Although women are routinely screened for postpartum depression in the United States, there is no recommended protocol to inform the identification of women who are likely to experience childbirth-related posttraumatic stress disorder. Advancements in computational methods of free text have shown promise in informing the diagnosis of psychiatric conditions. Although the language in narratives of stressful events has been associated with posttrauma outcomes, whether the narratives of childbirth processed via machine learning can be useful for childbirth-related posttraumatic stress disorder screening is unknown. OBJECTIVE: This study aimed to examine the use of written narrative accounts of personal childbirth experiences for the identification of women with childbirth-related posttraumatic stress disorder. To this end, we developed a model based on natural language processing and machine learning algorithms to identify childbirth-related posttraumatic stress disorder via the classification of birth narratives. STUDY DESIGN: Overall, 1127 eligible postpartum women who enrolled in a study survey during the COVID-19 pandemic provided short written childbirth narrative accounts in which they were instructed to focus on the most distressing aspects of their childbirth experience. They also completed a posttraumatic stress disorder symptom screen to determine childbirth-related posttraumatic stress disorder. After the exclusion criteria were applied, data from 995 participants were analyzed. A machine learning-based Sentence-Transformers natural language processing model was used to represent narratives as vectors that served as inputs for a neural network machine learning model developed in this study to identify participants with childbirth-related posttraumatic stress disorder. RESULTS: The machine learning model derived from natural language processing of childbirth narratives achieved good performance (area under the curve, 0.75; F1 score, 0.76; sensitivity, 0.8; specificity, 0.70). Moreover, women with childbirth-related posttraumatic stress disorder generated longer narratives (t test results: t=2.30; p=.02) and used more negative emotional expressions (Wilcoxon test: sadness: p=8.90e-04; W=31,017; anger: p=1.32e-02; W=35,005.50) and death-related words (Wilcoxon test: p=3.48e-05; W=34,538) in describing their childbirth experience than those with no childbirth-related posttraumatic stress disorder. CONCLUSION: This study provided proof of concept that personal childbirth narrative accounts generated in the early postpartum period and analyzed via advanced computational methods can detect with relatively high accuracy women who are likely to endorse childbirth-related posttraumatic stress disorder and those at low risk. This suggests that birth narratives could be promising for informing low-cost, noninvasive tools for maternal mental health screening, and more research that used machine learning to predict early signs of maternal psychiatric morbidity is warranted.

Original languageEnglish
Article number100834
Number of pages9
JournalAmerican journal of obstetrics & gynecology MFM
Issue number3
StatePublished - Mar 2023

Bibliographical note

Funding Information:
S.D. was supported by grants from the National Institute of Child Health and Human Development (R21HD100817 and R01HD108619), and an Interim Support Funding (ISF) award from the Massachusetts General Hospital Executive Committee on Research. K.M.J. was supported by the Mortimer B. Zuckerman STEM Leadership Postdoctoral Fellowship Program. The sponsors were not involved in the study design; collection, analysis, or interpretation of data; writing of the report; or decision to submit this article for publication.

Publisher Copyright:
© 2022 Elsevier Inc.


  • birth
  • machine learning
  • maternal morbidity
  • mental disorders
  • mental health
  • obstetrical labor
  • parturition
  • peripartum period
  • postpartum
  • postpartum depression
  • stressor-related disorders
  • trauma


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