Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin.

Journal: Frontiers in pharmacology
Published Date: May 22, 2025

Abstract

BACKGROUND: Acute kidney injury (AKI) is a significant concern among hospitalized patients receiving aminoglycosides. Identifying the risk factors associated with aminoglycoside-induced AKI and developing machine learning models are imperative in clinical practice.

Authors

  • Pei Zhang
    School of Pharmacy, Lanzhou University, Lanzhou 730000, China.
  • Qiong Chen
    Departement of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/Affiliated Cancer Hospital of Xiangya School of Medicine, Changsha 410013, China.
  • Jiahui Lao
    Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
  • Juan Shi
    Department of Clinical Pharmacy, The First People's Hospital of Jinan, Jinan, China.
  • Jia Cao
    Institute of Toxicology, Facutly of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China.
  • Xiao Li
    Department of Inner Mongolia Clinical Medicine College, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
  • Xin Huang
    Department of ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.

Keywords

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