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.

翻译：肝移植是终末期肝病患者挽救生命的手术。肝移植面临两大挑战：为捐赠者寻找最佳匹配患者，以及确保不同亚群之间的移植公平性。当前的MELD评分系统评估患者90天内未接受器官移植的死亡风险。然而，供体-患者匹配还应考虑术后风险因素，如心血管疾病、慢性排斥反应等，这些都是移植后的常见并发症。准确预测这些风险评分仍是一项重大挑战。本研究采用预测模型解决上述挑战。具体而言，我们提出了一种深度学习模型来预测肝移植后的多种风险因素。通过将其构建为多任务学习问题，该深度神经网络经训练可同时预测五种术后风险，并利用任务平衡技术实现同等优异的性能。我们还提出了一种新颖的公平性算法，以确保跨不同亚群的预测公平性。研究使用了1987年至2018年美国肝移植记录中收集的160,360名肝移植患者的电子健康记录，包括人口统计学信息、临床变量和实验室检测值。模型性能采用AUROC和AUPRC等多种性能指标进行评估。实验结果凸显了我们的多任务模型在保持准确性的同时实现任务平衡的成功性，该模型将任务差异降低了39%。进一步应用公平性算法显著减少了各风险因子在所有敏感属性（性别、年龄组和种族/民族）上的公平性差异。