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

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.

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Can Li, Xiaoqian Jiang, Kai Zhang (2024). Dataset: A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors. https://doi.org/10.57702/gaqnwxgo

DOI retrieved: December 16, 2024

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Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.1016/j.jbi.2023.104545
Author Can Li
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Xiaoqian Jiang
Kai Zhang
Homepage https://doi.org/10.1016/j.tran.2023.03.014