Decision Support System for Renal Transplantation
This work addresses the critical issue of reducing mortality rates in kidney transplantation for healthcare providers and patients, but it appears incremental as it applies existing prediction methods to a specific medical dataset.
The paper tackles the problem of mismatched deceased donor-recipient kidneys leading to post-transplant deaths by developing a prediction model to determine the success probability of renal transplantation, using data from 584 imported kidneys across 12 transplant centers.
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