A new utility-based framework optimizes performance-fairness trade-offs in decisions by modeling decision-maker and decision-subject utilities and using a social planner's utility to capture group inequalities under different justice principles.
Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.Journal of Translational Medicine
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Machine learning models on a novel Romanian EHR dataset of 12,286 sepsis hospitalizations achieve AUC 0.983 for death versus recovery prediction and identify eosinopenia as a top predictor via SHAP.
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First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs
A new utility-based framework optimizes performance-fairness trade-offs in decisions by modeling decision-maker and decision-subject utilities and using a social planner's utility to capture group inequalities under different justice principles.
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Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset
Machine learning models on a novel Romanian EHR dataset of 12,286 sepsis hospitalizations achieve AUC 0.983 for death versus recovery prediction and identify eosinopenia as a top predictor via SHAP.