A Survival-Driven Machine Learning Framework for Donor-Recipient Matching in Liver Transplantation: Predictive Ranking and Optimal Donor Profiling

Abstract

Liver transplantation is a life-saving treatment for patients with end-stage liver disease. However, donor organ scarcity and patient heterogeneity make finding the optimal donor-recipient matching a persistent challenge. Existing models and clinical scores are shown to be ineffective for large national datasets such as the United Network for Organ Sharing (UNOS). In this study, I present a comprehensive machine-learning-based approach to predict posttransplant survival probabilities at discrete clinical important time points and to derive a ranking score for donor-recipient compatibility. Furthermore, I developed a recipient-specific "optimal donor profile," enabling clinicians to quickly compare waiting-list patients to their ideal standard, streamlining allocation decisions. Empirical results demonstrate that my score’s discriminative performance outperforms traditional methods while maintaining clinical interpretability. I further validate that the top compatibility list generated by our proposed scoring method is non-trivial, demonstrating statistically significant differences from the list produced by the traditional approach. By integrating these advances into a cohesive framework, our approach supports more nuanced donor-recipient matching and facilitates practical decision-making in real-world clinical settings.