Methods for phenotyping adult patients with acute kidney injury: a systematic review.

Journal: Journal of nephrology
PMID: 39283471

Abstract

BACKGROUND: Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to customize treatments and predict disease trajectories.

Authors

  • Anjay P Shah
    Intelligent Clinical Care Center, University of Florida, Gainesville, FL, USA.
  • William Snead
    Intelligent Clinical Care Center, University of Florida, Gainesville, FL, USA.
  • Anshul Daga
    Intelligent Clinical Care Center, University of Florida, Gainesville, FL, USA.
  • Rayon Uddin
    Intelligent Clinical Care Center, University of Florida, Gainesville, FL, USA.
  • Esra Adiyeke
    University of Florida Intelligent Critical Care Center, Gainesville, FL; Department of Medicine, Division of Nephrology, Hypertension, and Renal Transplantation, University of Florida, Gainesville, FL.
  • Tyler J Loftus
    Department of Surgery, University of Florida Health, Gainesville, FL. Electronic address: tyler.loftus@surgery.ufl.edu.
  • Azra Bihorac
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Yuanfang Ren
    Department of Medicine, University of Florida, Gainesville, FL USA.
  • Tezcan Ozrazgat-Baslanti
    Department of Medicine, University of Florida, Gainesville, FL USA.

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