Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant.
翻译:器官移植是一些终末期疾病,如肝衰竭的基本治疗方法。分析器官移植后的死因(CoD)为临床决策提供了有力的工具,包括个性化治疗和器官分配。然而,由于两个主要的数据和模型相关挑战,传统方法如终末期肝病(MELD)评分和常规机器学习(ML)方法在CoD分析中受到限制。为了解决这个问题,我们提出了一个名为CoD-MTL的新框架,利用多任务学习来联合建模各种CoD预测任务之间的语义关系。具体而言,我们开发了一种新的多任务学习的树蒸馏策略,结合了树模型和多任务学习的优点。实验结果表明了我们框架精确可靠的CoD预测能力。进行了肝移植的案例研究,展示了我们的方法在临床上的重要性。