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：器官移植是某些终末期疾病（如肝衰竭）的重要治疗方法。分析器官移植后的死亡原因（CoD）为临床决策提供了有力工具，包括个性化治疗和器官分配。然而，传统方法（如终末期肝病模型（MELD）评分）和传统机器学习方法在死亡原因分析中存在局限，主要面临数据和模型相关的两大挑战。为解决此问题，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死亡原因预测任务之间的语义关系。具体而言，我们开发了一种新颖的多任务学习树蒸馏策略，该策略结合了树模型和多任务学习的优势。实验结果表明，我们的框架能够提供精确可靠的死亡原因预测。一项案例研究展示了该方法在肝移植中的临床重要性。