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A machine learning-based approach for predicting renal function recovery in general ward patients with acute kidney injury.

Nam-Jun ChoInyong JeongYeongmin KimDong Ok KimSe-Jin AhnSang-Hee KangHyo-Wook GilHwamin Lee
Published in: Kidney research and clinical practice (2024)
This study presented a machine learning approach for predicting renal function recovery in patients with AKI. The model performance was assessed across distinct hospital settings, which revealed its efficacy. Although the model exhibited favorable outcomes, the necessity for further enhancements and the incorporation of more diverse datasets is imperative for its application in real- world.
Keyphrases
  • machine learning
  • acute kidney injury
  • cardiac surgery
  • artificial intelligence
  • big data
  • single cell
  • emergency department
  • deep learning
  • rna seq