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May 13, 2022Journal of Translational Medicine362 citationsOpen Access

Machine learning for the prediction of acute kidney injury in patients with sepsis

SYSuru YueSLShasha LiXHXueying Huang

Key Points

  • To assess the predictive capability of machine learning algorithms for identifying acute kidney injury in patients diagnosed with sepsis.
  • Evaluated machine learning models, including XGBoost, to predict acute kidney injury occurrence in septic patients.
  • Machine learning models reliably predict the development of acute kidney injury in patients with sepsis.
  • The XGBoost algorithm achieved the highest predictive performance, providing a viable tool for early clinical risk assessment.

Abstract

The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

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Cite This Study

Yue et al. (2022) studied this question.

synapsesocial.com/papers/69d8beacce048d2571bedfcbhttps://doi.org/10.1186/s12967-022-03364-0
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