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

Jialin LiuJinfa WuSiru LiuMengdie LiKunchang HuKe Li
Published in: PloS one (2021)
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.
Keyphrases
  • machine learning
  • randomized controlled trial
  • acute kidney injury
  • intensive care unit
  • cardiovascular events
  • artificial intelligence
  • risk factors
  • type diabetes
  • mechanical ventilation