ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024
Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Machine Learning to Identify Point-of-Care Ultrasound and Evaluate Standardized Documentation: Retrospective Operational Cohort Study.Journal of medical Internet research · 2026Article
- The bottleneck was never data or algorithms: building a learning utility for AI-enabled learning health systems.npj health systems · 2026Article
- Implementation of a clinical decision support tool for postpartum depression: protocol for a prospective randomised clinical trial.BMJ open · 2026Article
- ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.medRxiv : the preprint server for health sciences · 2026Article
- AI for Detecting and Predicting Postpartum Depression: Scoping Review.Journal of medical Internet research · 2026Article
- Prevalence and Trajectories of Perinatal Anxiety and Depression in a Large Urban Medical Center.JAMA network open · 2025Article
- Aligning incentives: the importance of behavioral economic perspectives in AI adoption.npj health systems · 2025Article
- Predictive Analysis of Postpartum Depression Using Machine Learning.Healthcare (Basel, Switzerland) · 2025Article
- Making shiny objects illuminating: the promise and challenges of large language models in U.S. health systems.npj health systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
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.
Identifiers
39444417PMC11497630What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.