2023/03/30 by Sirui Ding, Ding, Sirui, Qiaoyu Tan +13
Computer Science · Health Professions · Medicine · #Artificial Intelligence in Healthcare #Computers and Society (cs.CY) #FOS: Computer and information sciences #Liver Disease Diagnosis and Treatment #Machine Learning (cs.LG) #Machine Learning in Healthcare
paper · doi:10.48550/arxiv.2304.00012
openalex publication_date 2023/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.