3 papers
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
TECM*: A Data-Driven Assessment to Reinforcement Learning Methods and Application to Heparin Treatment Strategy for Surgical Sepsis
Jiang Liu, Yujie Li, Chan Zhou +11
Objective: Sepsis is a life-threatening condition caused by severe infection leading to acute organ dysfunction. This study proposes a data-driven metric and a continuous reward fu…
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…