3 papers
cs.LG2026
Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving
Xixi Tian, Di Wu, Xiang Liu +4
Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated lea…
cs.LG2025
Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets
Bozhi Dan, Di Wu, Ji Xu +5
In the intensive care setting, sepsis continues to be a major contributor to patient illness and death; however, its timely detection is hindered by the complex, sparse, and hetero…
cs.LG2025
End to End Autoencoder MLP Framework for Sepsis Prediction
Hejiang Cai, Di Wu, Ji Xu +5
Sepsis is a life threatening condition that requires timely detection in intensive care settings. Traditional machine learning approaches, including Naive Bayes, Support Vector Mac…