  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.
