CoChat
Sign Up
Cited by 44Maternal Mental Health During Pregnancy and PostpartumMental Health via WritingMaternal and Perinatal Health Interventions

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

Open access
Bartal, Alon, Jagodnik, Kathleen M., Chan, Sabrina J., Babu, Mrithula S., Dekel, Sharon·2023·American Journal of Obstetrics & Gynecology MFM
DOI 10.1016/j.ajogmf.2022.100834PMC PMC9995215

Published version of a preprint

Open this paper in CoChat — free account

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ajogmf.2022.100834

Cites

62 of 62 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. [5]Dekel S, Gilberston M, Orr S, Rauch S, Nellie W, Pitman R. Trauma and posttraumatic stress disorder. In Stern TA, Fava M, Wilens T, Rosenbaum JF (Eds.): Mass General Hosp Compreh Clin Psych 2nd Ed., Philadelphia, PA, Elsevier, 2016.
  2. [65]Mueller J, Thyagarajan A. Siamese recurrent architectures for learning sentence similarity. In Proc AAAI Conf Artif Intellig, 2016: 30(1).

Cited by

Sorted by citations

Related

Authors
Bartal, A., Jagodnik, K. M., Chan, S. J., Babu, M. S., & Dekel, S.
Affiliations
  • Bar-Ilan University (IL)
  • Massachusetts General Hospital (US)
  • Harvard University (US)
Corresponding author
Dekel, Sharon
ORCID
Venue
American Journal of Obstetrics & Gynecology MFM
Publisher
Elsevier BV
Published
2023-03
Volume
5
Issue
3
Pages
100834
ISSN
2589-9333 (ISSN-L)
DOI
10.1016/j.ajogmf.2022.100834
PMC
PMC9995215
OpenAlex
W4310910011
Semantic Scholar
90ebce66c381f3a5ddd10c26a312ed30f62d918b
Cited by
44
References
62
Citations by year
2026: 10 · 2025: 15 · 2024: 12 · 2023: 7
FWCI
7.51
Type
journal article
Language
en
Open access
Licence
Locations
  • American Journal of Obstetrics & Gynecology MFM (publishedVersion)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
National Institute of Child Health and Human Development (R01HD108619, R21HD100817)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Maternal Mental Health During Pregnancy and Postpartum
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Health Sciences › Medicine › Obstetrics and Gynecology › Maternal and Perinatal Health Interventions
Keywords
postpartum mental health, traumatic birth, birth narratives, natural language processing, maternal mental health screening, Sentence-BERT, neural network classification, postpartum women, COVID-19 pandemic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review