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Prediction Models for AKI in ICU: A Comparative Study.

Qing QianJinming WuJia Yang WangHaixia SunLei Yang
Published in: International journal of general medicine (2021)
LightGBM demonstrated the best capability for predicting AKI in the first 72 h of ICU admission. LightGBM and XGBoost showed great potential for clinical application owing to their high recall value. This study can provide references for artificial intelligence-powered clinical decision support systems for AKI early prediction in the ICU setting.
Keyphrases
  • artificial intelligence
  • acute kidney injury
  • clinical decision support
  • intensive care unit
  • mechanical ventilation
  • machine learning
  • big data
  • deep learning
  • risk assessment
  • human health
  • climate change