A dynamic model for predicting graft function in kidney recipients' upcoming follow up visits: A clinical application of artificial neural network.

Journal: International journal of medical informatics
Published Date: Nov 1, 2018

Abstract

BACKGROUND: Predicting the function of transplanted kidneys would help clinicians in individualized medical interventions. We aimed to develop and validate a predictive tool for a future value of estimated glomerular filtration rate (eGFR) at upcoming visits.

Authors

  • Parviz Rashidi Khazaee
    Electrical and Computer Engineering Department, Urmia University, Urmia, Iran.
  • Jamshid Bagherzadeh
    Electrical and Computer Engineering Department, Urmia University, Urmia, Iran.
  • Zahra Niazkhani
    Nephrology and Kidney Transplant Research Center, Urmia University of Medical Sciences, Urmia, Iran; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran. Electronic address: niazkhani.z@umsu.ac.ir.
  • Habibollah Pirnejad
    Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran; Patient Safety Research Center, Urmia University of Medical Sciences, Urmia, Iran; Erasmus School of Health Policy & Management (ESHPM), Erasmus University Rotterdam, Rotterdam, the Netherlands.

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