Federated Learning with Multi-Partner OneFlorida+ Consortium Data for Predicting Major Postoperative Complications

2026-03-17

AI Summary

This study addresses the tension between privacy preservation and model generalizability in multicenter healthcare data collaboration by proposing a federated learning–based approach for predicting severe postoperative complications and mortality risk. Leveraging retrospective surgical patient data from five institutions within the OneFlorida+ consortium, the authors developed and validated models to predict intensive care unit admission, mechanical ventilation, acute kidney injury, and in-hospital mortality—all without sharing raw patient data across sites. The results demonstrate that the federated learning models achieve area under the receiver operating characteristic curve (AUROC) and area under the precision–recall curve (AUPRC) performance comparable to, and in some cases superior to, those of centralized models, while maintaining strong generalizability. This work thus offers a viable pathway for deploying clinical decision support systems in privacy-sensitive settings.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a large multicenter dataset from the OneFlorida Data Trust. We hypothesize that federated learning models will offer robust generalizability while preserving data privacy and security. Methods: This retrospective, longitudinal, multicenter cohort study included 358,644 adult patients admitted to five healthcare institutions, who underwent 494,163 inpatient major surgical procedures from 2012-2023. We developed and internally and externally validated federated learning models to predict the postoperative risk of intensive care unit (ICU) admission, mechanical ventilation (MV) therapy, acute kidney injury (AKI), and in-hospital mortality. These models were compared with local models trained on data from a single center and central models trained on a pooled dataset from all centers. Performance was primarily evaluated using area under the receiver operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPRC) values. Results: Our federated learning models demonstrated strong predictive performance, with AUROC scores consistently comparable or superior performance in terms of AUROC and AUPRC across all outcomes and sites. Our federated learning models also demonstrated strong generalizability, with comparable or superior performance in terms of both AUROC and AUPRC compared to the best local learning model at each site. Conclusions: By leveraging multicenter data, we developed robust, generalizable, and privacy-preserving predictive models for major postoperative complications and mortality. These findings support the feasibility of federated learning in clinical decision support systems.

Problem

• Federated Learning
• Postoperative Complications
• Multicenter Data
• Data Privacy
• Predictive Modeling

Innovation

• Federated Learning
• Multi-center Data
• Postoperative Complications Prediction
• Privacy-preserving AI
• Clinical Decision Support