Improving precision of glomerular filtration rate estimating model by ensemble learning.

Journal: Journal of translational medicine
PMID: 29121946

Abstract

BACKGROUND: Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise.

Authors

  • Xun Liu
    Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China. naturestyle@163.com.
  • Ningshan Li
    SJTU-YALE Joint Center for Biostatistics, Shanghai JiaoTong University, Shanghai, China.
  • Linsheng Lv
    Operating Room, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Yongmei Fu
    Emergency Department, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Cailian Cheng
    Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
  • Caixia Wang
    Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
  • Yuqiu Ye
    Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
  • Shaomin Li
    Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
  • Tanqi Lou
    Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China. lou.tq@163.com.

Keywords

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