Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study.

Journal: International urology and nephrology
PMID: 33710531

Abstract

BACKGROUND: Accurate estimating glomerular filtration rate (GFR) is crucial both in clinical practice and epidemiological survey. We incorporated semi-supervised learning technology to improve GFR estimation performance.

Authors

  • Ningshan Li
    SJTU-YALE Joint Center for Biostatistics, Shanghai JiaoTong University, Shanghai, China.
  • Hui Huang
    Department of Biobank, The Sixth Affiliated People's Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Lv Linsheng
    Operation Room, The Third Affiliated Hospital of Sun Yat-Sen University, Guangdong, China.
  • Hui Lu
    Key Laboratory of the plateau of environmental damage control, Lanzhou General Hospital of Lanzhou Military Command, Lanzhou, China.
  • Xun Liu
    Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China. naturestyle@163.com.

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