Building a Machine Learning Model to Predict Postpartum Depression from Electronic Health Records in a Tertiary Care Setting.

ABSTRACT: Background: Postpartum depression is a common mental health condition that can occur up to one year after childbirth. Recent studies have increasingly used machine learning techniques to predict its occurrence; however, few have comprehensively explored the use of electronic health record data, particularly in tertiary care settings where such data can be fragmented. Methods: We analyzed electronic health record data from 12,284 women who delivered at The Birth Center at Atrium Health Wake Forest Baptist Medical Center, excluding those with missing data or no prenatal or postpartum visits. To define the target variable, we examined different combinations of depression screening tools (Edinburgh Postnatal Depression Scale and Patient Health Questionnaire-9), along with diagnos

SUBMITTER: Ma Z 

PROVIDER: S-EPMC12470393 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature