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September 23, 2025Open Access

Interpretable Machine Learning for Early Prediction of Acute Kidney Disease (AKD) in Sepsis-Associated Acute Kidney Injury (SA-AKI): A Multicenter Cohort Study with External Validation

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Authors

SCShuang ChenGLGuang LiQZQiyi Zeng

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Overview

Cohort study finds machine learning enhances prediction of acute kidney disease in sepsis, suggesting improved clinical outcomes.

Key Points

  • Machine learning models accurately predict acute kidney disease progression in sepsis patients, improving clinical decision-making.
  • Gradient Boosting showed the highest prediction accuracy (78.94%), indicating its effectiveness for this condition.
  • Data from MIMIC-IV and eICU-CRD databases were analyzed, employing methods like Boruta and LASSO for feature selection.
  • Overfitting risks were identified through external validation, highlighting the need for cautious model application in clinical settings.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68d43d75713b0b5dfea7d5e2https://doi.org/10.21203/rs.3.rs-7313497/v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study2025
  2. 2Machine learning model for predicting the risk of AKI in early hemodynamically stable sepsis patients: a study based on the MIMIC IV database2026
  3. 3Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study (Preprint)2025
  4. 4Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review2025 · 7 citations
  5. 5Risk stratification for in-hospital mortality in sepsis-associated acute kidney injury patients receiving continuous renal replacement therapy: an interpretable, externally validated machine learning study2026