English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Liver transplant is an essential therapy performed for severe liver diseases.The fact of scarce liver resources makes the organ assigning crucial. Model forEnd-stage Liver Disease (MELD) score is a widely adopted criterion when makingorgan distribution decisions. However, it ignores post-transplant outcomes andorgan/donor features. These limitations motivate the emergence of machinelearning (ML) models. Unfortunately, ML models could be unfair and trigger biasagainst certain groups of people. To tackle this problem, this work proposes afair machine learning framework targeting graft failure prediction in livertransplant. Specifically, knowledge distillation is employed to handle denseand sparse features by combining the advantages of tree models and neuralnetworks. A two-step debiasing method is tailored for this framework to enhancefairness. Experiments are conducted to analyze unfairness issues in existingmodels and demonstrate the superiority of our method in both prediction andfairness performance.

Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning