A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors

arXiv:2304.02780v2 Announce Type: replace
Abstract: Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications …

abstract arxiv challenges cs.lg current deep learning disease equity life patient patients risk saving scoring stage transformer type