Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records.

This study describes the deployment process of an AI-driven clinical decision support (CDS) system to support postpartum depression (PPD) prevention, diagnosis and management. Central to this CDS is an L2-regularized logistic regression model trained on electronic health record (EHR) data at an academic medical center, and subsequently refined through a broader dataset from a consortium to ensure its generalizability and fairness. The deployment architecture leveraged Microsoft Azure to facilitate a scalable, secure, and efficient operational framework.