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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 take into consideration 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 will use predictive models
to solve the above challenge. We propose a deep learning framework 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 on
this data to simultaneously predict the five post-transplant risks and achieve
equally good performance by leveraging task balancing techniques. We also
propose a novel fairness achieving algorithm and 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 performance of the model was
evaluated using various performance metrics such as AUROC, AURPC, and accuracy.
The results of our experiments demonstrate that the proposed multitask
prediction model achieved high accuracy and good balance in predicting all five
post-transplant risk factors, with a maximum accuracy discrepancy of only 2.7%.
The fairness-achieving algorithm significantly reduced the fairness disparity
compared to the baseline model.