Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning

Authors

Sirui Ding
Ruixiang Tang
Daochen Zha
Na Zou
Kai Zhang
Xiaoqian Jiang
Xia Hu

Publication Date

1-1-2022

Abstract

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.

Keywords

Humans, End Stage Liver Disease, Liver Transplantation, Severity of Illness Index, Neural Networks, Computer, Machine Learning, Retrospective Studies

PMID

37128420

PMCID

PMC10148275

PubMedCentral® Posted Date

4-29-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes