Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach.

Journal: Journal of biomedical informatics
Published Date: May 18, 2023

Abstract

OBJECTIVE: Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity among patients with this condition. Identification of AKI subphenotypes could enable targeted interventions and a deeper understanding of the injury's pathophysiology. While previous approaches based on unsupervised representation learning have been used to identify AKI subphenotypes, these methods cannot assess time series or disease severity.

Authors

  • Yongsen Tan
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
  • Jiahui Huang
  • Jinhu Zhuang
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
  • Haofan Huang
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
  • Song Jiang
    Department of Intensive Care Unit, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
  • Miaowen She
    Department of Ultrasonography, Shenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, China.
  • Mu Tian
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
  • Yong Liu
    Department of Critical care medicine, Shenzhen Hospital, Southern Medical University, Guangdong, Shenzhen, China.
  • Xiaxia Yu
    School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.

Keywords

External Resources

Popular Topics
Recent Journals