Women with PMOS have significantly higher rates of depression and anxiety. Routine screening and integrated care are essential for improving psychiatric and reproductive outcomes.
AI and machine learning models, using tools like the EPDS and data from the electronic health record, may help clinicians detect and even prevent postpartum depression.
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Dr. Lee S. Cohen, Director of the Ammon-Pinizzotto Center for Women's Mental Health at Massachusetts General Hospital, recently shared his insights on optimizing the likelihood of treatment for postpartum depression with Ob.Gyn News on August 27, 2024.
Dr. Lee S. Cohen, Director of the Ammon-Pinizzotto Center for Women's Mental Health at Massachusetts General Hospital, recently shared his insights on access to perinatal mental healthcare with Ob.Gyn News on May 23, 2024.
Women who experienced depression during pregnancy were about four times as likely to experience PPD compared to women with no depression during pregnancy, with greater risk associated with depression during the first and third trimesters.
Childbirth-related PTSD is relatively common; routine screening with a survey of PTSD symptoms may identify women in need of specific PTSD interventions.
Machine learning can be used to analyze data contained within the electronic health record to accurately predict which individuals are at increased risk for postpartum depression.
Compared to individuals with histories of psychiatric illness prior to pregnancy, perinatal individuals without a prepregnancy psychiatric history were less likely to be screened, referred, and treated for depression.
Imagine if we could use machine learning to enhance the care we deliver, analyzing a larger body of patient-specific factors in order to make more reliable predictions of risk for perinatal mood and anxiety disorders.
