Liver Transplant

Liver transplantation, a life-saving procedure for end-stage liver disease, faces challenges in optimizing organ allocation and predicting post-transplant outcomes. Current research focuses on developing sophisticated predictive models, employing machine learning techniques like deep neural networks and multi-task learning, to improve risk stratification for graft failure and mortality, while simultaneously addressing potential biases in these predictions across different patient subpopulations. These advancements aim to enhance both the fairness and accuracy of organ allocation and post-transplant care, ultimately improving patient survival and quality of life.