Machine learning algorithm for early detection of end-stage renal disease.

Journal: BMC nephrology
Published Date: Nov 27, 2020

Abstract

BACKGROUND: End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database.

Authors

  • Zvi Segal
    Diagnostic Robotics Inc., Ariel, Israel.
  • Dan Kalifa
    Diagnostic Robotics Inc., Ariel, Israel.
  • Kira Radinsky
    Department of Computer Science , Technion - Israel Institute of Technology , Haifa 3200003 , Israel.
  • Bar Ehrenberg
    Diagnostic Robotics Inc., Ariel, Israel.
  • Guy Elad
    Diagnostic Robotics Inc., Ariel, Israel.
  • Gal Maor
    Diagnostic Robotics Inc., Ariel, Israel.
  • Maor Lewis
    Diagnostic Robotics Inc., Ariel, Israel.
  • Muhammad Tibi
    Diagnostic Robotics Inc., Ariel, Israel.
  • Liat Korn
    Ariel University, Ariel, Israel.
  • Gideon Koren
    From the Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (A.A.B., M.C., Y.S., A.S., A.H., R.M., E.B., S.N., E.K., Y.G., M.R.Z.); MaccabiTech, MKM, Maccabi Healthcare Services, Tel Aviv, Israel (E.H., G.K., V.S.); and Department of Imaging, Assuta Medical Centers, Tel Aviv, Israel (M.G.).

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