Effects of environmental phenols on eGFR: machine learning modeling methods applied to cross-sectional studies.

Journal: Frontiers in public health
PMID: 39148651

Abstract

PURPOSE: Limited investigation is available on the correlation between environmental phenols' exposure and estimated glomerular filtration rate (eGFR). Our target is established a robust and explainable machine learning (ML) model that associates environmental phenols' exposure with eGFR.

Authors

  • Lei Liu
    Department of Science and Technology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Hao Zhou
    State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan, Hubei 430030, China.
  • Xueli Wang
    Department of Pathology, Qingdao Eighth People's Hospital, Qingdao, China.
  • Fukang Wen
    Institute of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China.
  • Guibin Zhang
    College of Electronic and Information Engineering, Tongji University, Shanghai, China.
  • Jinao Yu
    Institute of Computer Science and Engineering, University of Wisconsin-Madison, Madison, WI, United States.
  • Hui Shen
    College of Mechatronics and Automation, National University of Defense Technology, Changsha, China.
  • Rongrong Huang
    Department of Pharmacy, Affiliated Hospital of Nantong University, Nantong, China.

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