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May 30, 2026Frontiers in MedicineOpen Access

Machine learning model for predicting the risk of AKI in early hemodynamically stable sepsis patients: a study based on the MIMIC IV database

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Authors

MHMiao HeXLXinran LiJWJiajing Wu

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Overview

Randomized trial develops a machine learning model to predict AKI risk in hemodynamically stable sepsis patients, indicating its potential utility in clinical settings.

Key Points

  • The study aims to develop a machine learning model to predict the risk of acute kidney injury in hemodynamically stable sepsis patients.
  • Extracted clinical data from hemodynamically stable sepsis patients in the MIMIC IV Database.
  • Patients were randomly divided into a training set (70%) and a testing set (30%).
  • Models including XGBoost were constructed and assessed for performance using various metrics.
  • Out of 8,276 patients, 3,061 (37%) experienced acute kidney injury.
  • The XGBoost model exhibited optimal performance based on metrics including AUC and accuracy.
  • External validation results supported the model's predictive capabilities.

Cite This Study

He et al. (2026) studied this question.

synapsesocial.com/papers/6a1a7d8f0307b785094309f1https://doi.org/10.3389/fmed.2026.1846554
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Also Consider

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

  1. 1Interpretable 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 2025
  2. 2Risk stratification for in-hospital mortality in sepsis-associated acute kidney injury patients receiving continuous renal replacement therapy: an interpretable, externally validated machine learning study2026
  3. 3Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review2025 · 7 citations
  4. 4Machine learning-based risk prediction model development for acute kidney injury in type 2 myocardial infarction patients2025 · 1 citations
  5. 5Acute kidney injury severity in ICU patients: Developing and evaluating a data-driven analysis of clinical covariates using machine learning2026