Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US.

Journal: EBioMedicine
Published Date: Apr 26, 2025

Abstract

BACKGROUND: COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these risks post-pandemic have been absent. We aimed to use large electronic health records (EHR) and ML algorithms to predict the incidence of AKI and CKD during the post-pandemic period, assess the necessity of including COVID-19 infection history as a predictor, and develop a practical webpage application for clinical use.

Authors

  • Yue Zhang
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Nasrollah Ghahramani
    Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA; Department of Medicine, Penn State College of Medicine, Hershey, PA, USA.
  • Runjia Li
    School of Computer Science, China University of Geosciences, Wuhan 430074, China. lirunjia@cug.edu.cn.
  • Vernon M Chinchilli
    Department of Public Health Sciences, Pennsylvania State University, USA. Electronic address: vchinchilli@pennstatehealth.psu.edu.
  • Djibril M Ba
    Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA. Electronic address: djibrilba@pennstatehealth.psu.edu.

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