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

Suru Yue; Shasha Li; Xueying Huang; Jie Liu; Xuefei Hou; Yumei Zhao; Dongdong Niu; Yufeng Wang; Wenkai Tan; Jiayuan Wu

doi:10.1186/s12967-022-03364-0

May 13, 2022SHILAP Revista de lepidopterologíaOpen Access

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

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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.

Implication

Machine learning analysis demonstrates reliable prediction of acute kidney injury in sepsis patients using XGBoost, suggesting potential for early risk identification.

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Cite This Study

Yue et al. (2022) studied this question.

synapsesocial.com/papers/69d8beacce048d2571bedfcb https://doi.org/10.1186/s12967-022-03364-0

View Full Paper

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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