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

Authors

Can Li
Xiaoqian Jiang
Kai Zhang

Publication Date

1-1-2024

Journal

Journal of Biomedical Informatics

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 transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained to simultaneously predict the five post-transplant risks and achieve equal good performance by exploiting task-balancing techniques. We also proposed a novel fairness-achieving algorithm to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The model's performance was evaluated using various performance metrics such as AUROC and AUPRC. Our experiment results highlighted the success of our multi-task model in achieving task balance while maintaining accuracy. The model significantly reduced the task discrepancy by 39 %. Further application of the fairness-achieving algorithm substantially reduced fairness disparity among all sensitive attributes (gender, age group, and race/ethnicity) in each risk factor. It underlined the potency of integrating fairness considerations into the task-balancing framework, ensuring robust and fair predictions across multiple tasks and diverse demographic groups.

Keywords

Humans, United States, Liver Transplantation, Deep Learning, Tissue Donors, Neural Networks, Computer, Risk Factors, Fairness, Liver transplantation, Risk prediction, Multi-task learning

DOI

10.1016/j.jbi.2023.104545

PMID

37992791

PMCID

PMC11619923

PubMedCentral® Posted Date

12-5-2024

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes