Full website is coming soon

Pregnancy, birth and early childhood

Prediction of postpartum depression

Goal
To develop and validate a predictive model for postpartum depression (PPD) from EHR data
Impact
Early detection of PPD will allow early intervention and appropriate treatment, improving clinical outcomes of mother and child
Collaborator(s)
Weill Cornell Medicine, NY

Postpartum depression (PPD) is one of the most common complications of childbearing, estimated to affect between 10% and 15% of mothers worldwide. It is a leading cause of maternal perinatal mortality, and has a negative association with infant cognitive development, language development, and behaviors. The current screening routine is broadly based on identifying symptoms using self-reported questionnaires such as the Edinburgh Postnatal Depression Scale (EPDS).

We harness electronic health record (EHR) data of women who have given birth and identify PPD based on diagnosis codes, drug prescriptions, and non-pharmacological treatments. Using machine-learning techniques, we develop prediction models to identify women at risk. These models may improve current screening tools and facilitate early intervention.

Reference(s)

G. Amit, I. Girshovitz, P. Akiva, V. Bar, Y. Zhang, A. Hermann, R. Joly, M. Turchioe, J. Pathak. Enhancing the detection of postpartum depression from electronic health records using machine learning. 28th European Congress of Psychiatry (EPA) 2020.

Want to...





    This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.





      This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.





        This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.