A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors.

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 after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, we proposed a deep learning model to predict multiple risk factors after a liver

SUBMITTER: Li C 

PROVIDER: S-EPMC11619923 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature