AI Model Developed to Identify Post-Traumatic Stress Disorder Related to Childbirth
The rapid identification of new mothers at risk of CB-PTSD could help more women to treat their symptoms.
It is suggested that analyzing the narrative style and language used by patients when recounting traumatic events can offer deep insights into their mental well-being.
The words a woman uses to describe her birthing experience can potentially reflect post-traumatic adjustment before undergoing detailed psychological assessment.
The new model utilizes artificial intelligence (AI) to analyze the narrative and language in a patient’s birthing story, aiming to identify potential markers of CB-PTSD. Further refinements are needed to effectively and accurately detect the mental health condition.
The current diagnosis process of CB-PTSD involves two steps.
The second step includes these women undergoing a structured PTSD interview by a clinician, which is resource-intensive.
The study found that a machine learning model could effectively identify women at risk of CB-PTSD as an alternative to clinician interviews.
The study involved 1,295 participants who completed an anonymous web survey, including a questionnaire and a brief narrative of their birthing experience.
The success of the AI model in diagnosing CB-PTSD is attributed to its alignment with high questionnaire scores among participants.
What is Childbirth-Related Post-Traumatic Stress Disorder?
CB-PTSD arises shortly after childbirth and may cause the child to evoke traumatic memories in the mother.
This condition can lead to maternal attachment issues, hinder breastfeeding, affect early childhood development, and incur substantial public health costs.
However, the current screening for CB-PTSD faces challenges due to underreporting by women caused by shame, stigma, fear of separation from infants, and lack of awareness.
