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July 25, 2024BMC Medical Informatics and Decision MakingOpen Access

Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury

Le Li Jingyuan Guan Xi Peng Likun Zhou Zhuxin Zhang Ligang Ding Lihui Zheng Lingmin Wu Zhicheng Hu Limin Liu Yan Yao

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Abstract

Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI.

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Cite This Study

Li et al. (2024) studied this question.

synapsesocial.com/papers/68e5f0a8b6db64358758546d https://doi.org/10.1186/s12911-024-02583-3

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

1Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury2024 · 42 citations
2Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review2025 · 8 citations
3An interpretable machine-learning model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injuryCohort Study · 2026 · 2 citations
4Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury2024 · 17 citations
5Interpretable 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

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Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data2026
Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care2024
Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury2024

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