  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 multitask 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.
