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

Abstract

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. We used Fast Healthcare Interoperability Resources (FHIR) for data extraction and ingestion between the two systems. Continuous Integration/Continuous Deployment pipelines automated the deployment and ongoing maintenance, ensuring the system's adaptability to evolving clinical data. Along the technical preparation, we focused on a seamless integration of the CDS within the clinical workflow, presenting risk assessment directly within the clinician schedule and providing options for subsequent actions. The developed CDS is expected to drive a PPD clinical pathway to enable efficient PPD risk management.