A Multi-Task Learning Framework for Mortality Prediction in Liver Transplant Candidates

Current models for predicting waitlist mortality among liver transplant (LT) candidates primarily rely on conventional statistical regression-based approaches, which are typically developed separately for hepatocellular carcinoma (HCC) and non-HCC patients. These linear models may fail to capture complex, nonlinear relationships in the data, limiting their predictive performance. In this study, we evaluate and compare existing clinical scoring systems against various machine learning (ML) models, including both linear and nonlinear approaches, with a particular focus on a Multi-Task Learning (MTL) framework. Our results demonstrate that MTL outperforms both conventional methods and single-task learners across HCC and non-HCC groups. Furthermore, by leveraging SHapley Additive Explanations (SHAP), we provide deeper insights into the MTL model's decision-making process, offering both global and local explanations while pinpointing key risk factors for waitlist mortality in both patient groups. This study highlights the potential of advanced ML methodologies to improve LT organ allocation and underscores the need for their broader adoption in clinical practice, pending prospective validation.