Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.

Journal: BMC nephrology
PMID: 40264055

Abstract

BACKGROUND: Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learning approaches offer promising solutions for risk prediction, their clinical application often lacks interpretability. We developed an explainable artificial intelligence (XAI) based supervised learning model to predict 30-day hospital readmission risk following renal transplantation.

Authors

  • Nasser Alnazari
    Hepatobiliary Science and Organ Transplant Center, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia. Alnazarina@ngha.med.sa.
  • Omar Ibrahim Alanazi
    King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia.
  • Muath Owaidh Alosaimi
    King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia.
  • Ziyad Mohamed Alanazi
    King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia.
  • Ziyad Mohammed Alhajeri
    King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia.
  • Khaled Mohammed Alhussaini
    King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia.
  • Abdulkarim Mekhlif Alanazi
    King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia.
  • Ahmed Y Azzam
    Faculty of Medicine, October 6 University, Giza, Egypt.

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