Machine learning-optimized perinatal depression screening: Maximum impact, minimal burden.

Introduction: Perinatal depression affects up to 30% of pregnant and postpartum women, which has increased since the COVID-19 pandemic, making rapidly identifying affected women a high clinical priority. While screening tools like the Edinburgh Postnatal Depression Scale (EPDS) are widely used, brevity is important for busy clinical practice to reduce administration time and patient burden. Current methods to shorten assessments rely on traditional psychometric approaches, rather than machine learning (ML) methods that could optimize predictive accuracy. Methods: We developed a ML framework using National Clinical Cohort Collaborative (N3C) data to predict full 10-item EPDS scores from shortened question subsets (n=22,924). We evaluated all 2-5 item combinations using linear regression, validating performance across multiple cohorts including postpartum women (n=7,750) and an external non-N3C pregnancy population (n=1,217). For additional validation, we applied our approach to the PHQ-9 (n=398,606) to test generalizability. Binary classification models using clinical thresholds (≥13) determined EPDS screening accuracy. Decision curve analysis was performed to assess the clinical utility of our ML method. Results: The optimal 2-question EPDS combinations Q4+Q8 (anxiety/sadness) and Q5+Q8 (scared/sadness) both achieved R Conclusion/Implications: Our ML framework suggests a reduced assessment burden with two EPDS questions maintains predictive accuracy as the full-item EPDS. With ~3.6 million annual U.S. births, this approach could identify additional positive perinatal depression screens, enhancing screening implementation across clinical settings.