Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: One of the most prevalent complications of Partial Nephrectomy (PN) is Acute Kidney Injury (AKI), which could have a negative impact on subsequent renal function and occurs in up to 24.3% of patients undergoing PN. The aim of this study was to predict the occurrence of AKI following PN using preoperative parameters by applying machine learning algorithms.

Authors

  • Teddy Lazebnik
    Department of Mathematics, Ariel University, Ariel, Israel.
  • Zaher Bahouth
    Department of Urology, Bnai Zion Medical Center, Haifa, Israel.
  • Svetlana Bunimovich-Mendrazitsky
    Department of Mathematics, Ariel University, Ariel, Israel.
  • Sarel Halachmi
    Department of Urology, Bnai Zion Medical Center, Haifa, Israel.