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. 2024 Oct 21:2024:1057-1066.
eCollection 2024.

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

Yiye Zhang  1   2 , Rochelle Joly  1 , Ashley N Beecy  1   2 , Samen Principe  2 , Sujit Satpathy  2 , Anatoly Gore  2 , Tom Reilly  2 , Mitchel Lang  2 , Nagi Sathi  2 , Carlos Uy  2 , Matt Adams  2 , Mark Israel  2
Affiliations

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

Yiye Zhang et al. AMIA Jt Summits Transl Sci Proc. .

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.

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Figures

Figure 1.
Figure 1.
Deployment Structure
Figure 2.
Figure 2.
EHR interface
Figure 3.
Figure 3.
Proposed clinical workflow incorporating the CDS.
See this image and copyright information in PMC

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