Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant

• URL: http://arxiv.org/abs/2304.00012v3
• Date: Thu, 5 Oct 2023 21:38:54 GMT
• Title: Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
• Authors: Sirui Ding, Qiaoyu Tan, Chia-yuan Chang, Na Zou, Kai Zhang, Nathan R. Hoot, Xiaoqian Jiang, Xia Hu
• Abstract summary: Post-transplant cause of death provides powerful tool for clinical decision making. Traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis. We propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly.
• Score: 65.85767739748901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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