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October 17, 2025Frontiers in MedicineOpen Access

Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches

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Authors

ZQZhen QuanZHZheng HanSZSiyao Zeng

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Overview

This analysis demonstrates machine learning effectively predicts AKI in sepsis patients, suggesting advanced approaches can improve clinical outcomes.

Key Points

  • The Random Forest model showed optimal performance, with an average AUC score of 0.89 indicating strong predictive capability.
  • A total of 6,866 critically ill sepsis patients were analyzed, revealing that 5,896 developed acute kidney injury during hospitalization.
  • SHAP analysis was utilized to interpret results, highlighting key features like urine output and BMI that influence prediction accuracy.
  • Machine learning frameworks in this study enhance the ability to forecast AKI in sepsis patients, potentially improving clinical interventions.

Cite This Study

Quan et al. (2025) studied this question.

synapsesocial.com/papers/68f199c5de32064e504dcec9https://doi.org/10.3389/fmed.2025.1667488
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