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：器官移植是治疗某些终末期疾病（如肝衰竭）的关键方法。分析器官移植后的死因（CoD）为临床决策（包括个性化治疗和器官分配）提供了有力工具。然而，传统方法，如终末期肝病模型（MELD）评分和传统机器学习（ML）方法，在CoD分析中因数据与模型相关的两大挑战而受到限制。为解决这一问题，我们提出了一种名为CoD-MTL的新框架，利用多任务学习联合建模不同CoD预测任务之间的语义关系。具体来说，我们开发了一种新颖的多任务学习树蒸馏策略，结合了树模型与多任务学习的优势。实验结果展示了该框架在CoD预测中的精确性与可靠性。通过一个案例研究，我们证明了该方法在肝移植中的临床重要性。