February 4, 2021Open Access

Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model

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Resumen

XGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.

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Cite This Study

Liu et al. (Thu,) studied this question.

synapsesocial.com/papers/69dc236eea70a37eff955321 https://doi.org/https://doi.org/10.1371/journal.pone.0246306

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1Understanding Electronic AKI Alerts: Characterization by Definitional Rules2016 · 17 citations
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3Correlation of Thyroid Hormone Profile with the Acute Physiology and Chronic Health Evaluation II Score as a Prognostic Marker in Patients with Sepsis in the Intensive Care Unit.2018 · 6 citations
4Mortality Prediction in the ICU2016 · 22 citations
5Combining patient visual timelines with deep learning to predict mortality2019 · 25 citations

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