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

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.

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

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.

Traumatic childbirth during COVID-19 triggers maternal psychological growth and in turn better mother-infant bonding.

BACKGROUND: Although posttraumatic psychological growth (PTG) occurs following stressful events, knowledge of maternal psychological growth as a result of giving birth during the novel coronavirus (COVID-19) pandemic is lacking. METHODS: We assessed PTG associated with recent childbirth (Posttraumatic Growth Inventory) in a sample of 2205 women who gave birth during the pandemic and 540 who gave birth before. They also provided information about birth-related traumatic stress (Peritraumatic Distress Inventory; PTSD Checklist), mother-infant bonding (Maternal Attachment Inventory), and breastfeeding. RESULTS: Close to two thirds (60.45 %) of participants reported childbirth-related PTG with greater appreciation of life endorsed most frequently. No group differences in PTG prevalence were noted between deliveries during or before COVID-19 (χ2 = 0.35, p = 0.84). A multigroup mediation model revealed that in deliveries during the pandemic, childbirth-related acute stress was linked with elevated PTG (ß = 0.07, p < 0.01); in turn, PTG was associated with lower posttraumatic stress symptoms (ß = -0.06, p < 0.05) and better mother-infant bonding (ß = 0.22, p < 0.001). These indirect paths via PTG were not significant in deliveries before the pandemic. LIMITATIONS: Reliance on a convenient sample, self-reports, and cross-sectional design may introduce bias. CONCLUSIONS: Perceived positive maternal psychological changes as a result of childbirth are endorsed by a significant portion of women during the pandemic and can ensue in response to traumatic childbirth. Maternal growth is further implicated in successful postpartum adjustment and positive mother-infant interactions during an important period. Hence, directing clinical attention to opportunities of maternal psychological growth may have benefits especially for women at risk for the adverse outcomes of exposure to traumatic experiences of childbirth.