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Acute Kidney Injury Prediction Model Using Cystatin-C, Beta-2 Microglobulin, and Neutrophil Gelatinase-Associated Lipocalin Biomarker in Sepsis Patients.

Hani SusiantiAswoco Andyk Asmoronull SujarwotoWiwi JayaHeri SutantoAmanda Yuanita KusdijantoKevin Putro KuwoyoKristian HanantoMatthew Brian Khrisna
Published in: International journal of nephrology and renovascular disease (2024)
The Naïve Bayes machine learning model in this study is useful for predicting AKI in sepsis patients.
Keyphrases
  • acute kidney injury
  • machine learning
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • intensive care unit
  • prognostic factors
  • patient reported outcomes
  • artificial intelligence
  • patient reported