Sepsis Prediction

Sepsis prediction research aims to develop accurate and timely methods for identifying this life-threatening condition, improving patient outcomes through earlier intervention. Current efforts heavily utilize machine learning, employing algorithms like Random Forest, Gradient Boosting, and various neural networks (including convolutional and recurrent architectures) to analyze diverse data sources such as vital signs, lab results, and clinical notes. This work emphasizes model interpretability and fairness to enhance clinical trust and ensure equitable application across patient populations, ultimately aiming to reduce sepsis-related mortality and improve healthcare resource allocation.