Privacy-aware artificial intelligence framework for early sepsis detection in sustainable healthcare systems

Abstract

Sepsis is a life-threatening medical emergency which needs immediate treatment because its early diagnosis in intensive care units (ICUs) proves vital for reducing death rates. The study introduces a sepsis prediction system which protects user privacy through its use of personalized federated learning and differential privacy technology (pFL-DP) within a 5G-enabled intelligent healthcare system that enables both clinical AI growth and environmental preservation. The framework enables hospitals to develop shared models by training their systems together without needing to disclose any actual patient information which protects their data while maintaining complete privacy security. The hybrid feature selection which combines filter-based statistical techniques with wrapper-based optimization methods is implemented to improve the efficiency and interpretability and sustainability of computational resources for MIMIC-IV electronic health record data analysis. Three models: XGBoost, Bidirectional Long Short-Term Memory (Bi-LSTM), and a stacked ensemble—are evaluated under centralized and federated settings. Results show that the personalized federated ensemble achieves 90.1% accuracy, 87% sensitivity, and an average early warning lead time of 5.1 h prior to clinical diagnosis. Performance closely matches centralized learning while preserving patient privacy. Deployment simulations in a 5G-enabled edge–cloud environment demonstrate millisecond-level inference latency and reduced communication overhead, confirming the feasibility of secure, real-time, and sustainable AI-driven sepsis prediction in critical care environments.