Corrections to “Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients” | Synapse
April 7, 2026Open Access
Corrections to “Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients”
Key Points
The aim is to correct previous inaccuracies in survival prediction related to liver transplant patients using adversarial learning techniques.
Analysis of previously published data on liver transplant survival
Identification of errors in the original methods
Recommendations for improved data handling and model training
Clarifications on the applicability of adversarial learning in imbalanced data contexts
Refinement of survival prediction accuracy metrics
Suggestions for future research directions in medical data analysis
Abstract
Presents corrections to the paper, (Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients).