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

Language

English

Publication Date

1-1-2023

Journal

AMIA Annual Symposium Proceedings Archive

PMID

38222347

PMCID

PMC10785876

PubMedCentral® Posted Date

January 2024

PubMedCentral® Full Text Version

Post-print

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.

Keywords

Humans, Liver Transplantation, End Stage Liver Disease, Cause of Death, Tissue and Organ Procurement, Severity of Illness Index

Published Open-Access

yes

Recommended Citation

Ding, Sirui; Tan, Qiaoyu; Chang, Chia-Yuan; et al., "Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant" (2023). Faculty, Staff and Student Publications. 1217.
https://digitalcommons.library.tmc.edu/uthmed_docs/1217