Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept.

Journal: Burns : journal of the International Society for Burn Injuries
PMID: 31230801

Abstract

BACKGROUND: Burn critical care represents a high impact population that may benefit from artificial intelligence and machine learning (ML). Acute kidney injury (AKI) recognition in burn patients could be enhanced by ML. The goal of this study was to determine the theoretical performance of ML in augmenting AKI recognition.

Authors

  • Nam K Tran
    Dept. of Pathology and Laboratory Medicine, United States. Electronic address: nktran@ucdavis.edu.
  • Soman Sen
    Division of Burn Surgery, Dept. of Surgery, United States.
  • Tina L Palmieri
    Division of Burn Surgery, Dept. of Surgery, United States.
  • Kelly Lima
    Dept. of Pathology and Laboratory Medicine, United States.
  • Stephanie Falwell
    Dept. of Pathology and Laboratory Medicine, United States.
  • Jeffery Wajda
    UC Davis Health, United States.
  • Hooman H Rashidi
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Electronic address: rashidihh@upmc.edu.

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