Interpretable AI Model Predicts 28-Day Mortality in Sepsis-Linked Kidney Injury
An interpretable XGBoost model achieved an AUC of 0.881 for predicting 28-day mortality in critically ill patients with sepsis-associated acute ...
An interpretable XGBoost model achieved an AUC of 0.881 for predicting 28-day mortality in critically ill patients with sepsis-associated acute ...
A large pooled European cohort study with US validation finds that an elevated red cell distribution width, a standard component ...
The days immediately following a hospital stay are among the most dangerous in the life of a patient with metastatic ...
In recent years, the economic valuation of public health interventions has become a cornerstone in policy discussions, particularly when evaluating ...
In a significant stride toward revolutionizing how we understand aging, scientists at the University of Washington School of Medicine have ...
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© 2025 Scienmag - Science Magazine