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Interpretable Machine Learning Model for Early Mortality Prediction in Septic Patients Using Routine Post-Diagnosis Clinical Data: A Multicenter Study
0
Zitationen
7
Autoren
2025
Jahr
Abstract
We developed a machine learning model to predict the risk of early 7-day mortality in sepsis patients based on routine clinical data obtained immediately after diagnosis and validated its potential as a clinically reliable tool, achieving an AUROC of 0.767 in the training set. The use of SHAP-based interpretation enhances model interpretability, enabling clinicians to better understand the factors influencing mortality, identify high-risk patients early, and implement timely interventions to improve outcomes.
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