Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study.

Journal: PloS one
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are frequent complications of hospitalization, resulting in reduced outcomes and increased cost burden. However, these conditions are only sometimes recognized and promptly treated. Leveraging electronic health records (EHR), we explored the potential of artificial intelligence (AI) in diagnosing AKI and AKD during hospitalization.

Authors

  • Lorenzo Ruinelli
    Information and Communications Technology, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.
  • Pietro Cippà
    Division of Nephrology, Ente Ospedaliero Cantonale, Lugano, Switzerland.
  • Chantal Sieber
    Division of Nephrology, Ente Ospedaliero Cantonale, Lugano, Switzerland.
  • Clelia Di Serio
    Clinical Trial Unit, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.
  • Paolo Ferrari
    Department of Medicine, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.
  • Antonio Bellasi
    Division of Nephrology, Ente Ospedaliero Cantonale, Lugano, Switzerland.

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