Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
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DOI：
10.48550/arxiv.2304.00012
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Sirui Ding;Qiaoyu Tan;Chia-yuan Chang;Na Zou;Kai Zhang;N. Hoot;Xiaoqian Jiang;Xia Hu
中科院分区：
文献类型：
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作者：
Sirui Ding;Qiaoyu Tan;Chia-yuan Chang;Na Zou;Kai Zhang;N. Hoot;Xiaoqian Jiang;Xia Hu
器官移植是治疗某些终末期疾病（如肝功能衰竭）的必要手段。分析器官移植后的移植后死亡原因（CoD）为临床决策提供了强有力的工具，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的挑战，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分析中受到限制。为了解决这个问题，我们提出了一个新的框架称为CoD-MTL利用多任务学习模型的各种CoD预测任务之间的语义关系联合。具体来说，我们开发了一种新的树蒸馏策略的多任务学习，它结合了树模型和多任务学习的优势。实验结果表明，我们的框架的精确和可靠的CoD预测。一个案例研究进行证明我们的方法在肝移植的临床重要性。
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.