Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives

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 a traumatic childbirth and develop posttraumatic stress disorder (PTSD) symptoms following delivery (CB-PTSD). Although women are routinely screened for postpartum depression in the U.S., there is no recommended protocol to inform the identification of women who are likely to experience CB-PTSD. Advancements in computational methods of free text has shown promise in informing diagnosis of psychiatric conditions. Although the language in narratives of stressful events has been associated with post-trauma outcomes, whether the narratives of childbirth processed via machine learning can be useful for CB-PTSD screening is unknown.

Objective: This study examined the utility of written narrative accounts of personal childbirth experience for the identification of women with provisional CB-PTSD. To this end, we developed a model based on natural language processing (NLP) and machine learning (ML) algorithms to identify CB-PTSD via classification of birth narratives.

Study design: A total of 1,127 eligible postpartum women who enrolled in a study survey during the COVID-19 era 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 PTSD symptom screen to determine provisional CB-PTSD. After exclusion criteria were applied, data from 995 participants was analyzed. An ML-based Sentence-Transformer NLP model was used to represent narratives as vectors that served as inputs for a neural network ML model developed in this study to identify participants with provisional CB-PTSD.

Results: The ML model derived from NLP of childbirth narratives achieved good performance: AUC 0.75, F1-score 0.76, sensitivity 0.8, and specificity 0.70. Moreover, women with provisional CB-PTSD generated longer narratives (t-test results: t=2 . 30, p=0 . 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 CB-PTSD.

Conclusions: This study provides 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 CB-PTSD and those at low risk. This suggests that birth narratives could be promising for informing low-cost, non-invasive tools for maternal mental health screening, and more research that utilizes ML to predict early signs of maternal psychiatric morbidity is warranted.

Figure 1.

Number of words in childbirth narratives by childbirth-related PTSD status. Boxplots display word count in narratives for CB-PTSD (Class 1, PCL-5 ≥ 31, pink) and No CB-PTSD (Class 0, PCL-5 < 31, light blue). Dots are data points (narratives’ word counts) shifted by a random value. The mean word count (WC) for Class 1 is 191.91, and for Class 0 is 142. The median WC for Class 1 is 154.61, and for Class 0 is 106. A t-test revealed that participants of Class 1 used more words to depict their birth narrative than those of Class 0 (t = 2.30, df = 111.99, p = 0.02).

Figure 2.

Frequency of words in childbirth narratives by childbirth-related PTSD status. Distribution of word frequencies (LIWC value) per CB-PTSD Class (Class 1, CB-PTSD, pink; and Class 0, No CB-PTSD, light blue). PTSD was measured by PTSD Checklist for DSM-5 (PCl-5 ≥ 31). The table in the figure elaborates significant results of a Wilcoxon rank sum test with continuity correction between a word category in Class 0 and Class 1. X-axis label ‘i’ is the first-person pronoun “I”.