Enhancing Fairness and Explainability in Sepsis Mortality Prediction Models

This study proposes a method that learns a fair sepsis mortality predictive model by applying transfer learning from a performance-optimized model, and introduces a novel permutation-based feature importance algorithm to elucidate how each feature contributes to improving fairness across different races.