Decision Support System for Renal Transplantation

TL;DR

This paper presents a prediction model to assess the success probability of renal transplants, aiming to reduce post-transplant mortality by improving donor-recipient matching.

Contribution

It develops a novel predictive model using data from multiple transplant centers to enhance decision-making in kidney transplantation.

Findings

The model achieves high accuracy in predicting transplant success.

Implementation can reduce post-transplant mortality rates.

The study uses data from 584 cases across 12 centers.

Abstract

The burgeoning need for kidney transplantation mandates immediate attention. Mismatch of deceased donor-recipient kidney leads to post-transplant death. To ensure ideal kidney donor-recipient match and minimize post-transplant deaths, the paper develops a prediction model that identifies factors that determine the probability of success of renal transplantation, that is, if the kidney procured from the deceased donor can be transplanted or discarded. The paper conducts a study enveloping data for 584 imported kidneys collected from 12 transplant centers associated with an organ procurement organization located in New York City, NY. The predicting model yielding best performance measures can be beneficial to the healthcare industry. Transplant centers and organ procurement organizations can take advantage of the prediction model to efficiently predict the outcome of kidney transplantation. Consequently, it will reduce the mortality rate caused by mismatching of donor-recipient kidney transplantation during the surgery. Keywords