Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
This work addresses fairness issues in organ allocation for liver transplant patients, which is an incremental improvement over existing models.
The paper tackles the problem of unfairness in machine learning models predicting graft failure for liver transplant organ assignment by proposing a fair ML framework that uses knowledge distillation and a two-step debiasing method, achieving superior prediction and fairness performance in experiments.
Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse features by combining the advantages of tree models and neural networks. A two-step debiasing method is tailored for this framework to enhance fairness. Experiments are conducted to analyze unfairness issues in existing models and demonstrate the superiority of our method in both prediction and fairness performance.