Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.
Journal:
Transplantation
Published Date:
Apr 1, 2017
Abstract
BACKGROUND: The ability to predict graft failure or primary nonfunction at liver transplant decision time assists utilization of scarce resource of donor livers, while ensuring that patients who are urgently requiring a liver transplant are prioritized. An index that is derived to predict graft failure using donor and recipient factors, based on local data sets, will be more beneficial in the Australian context.
Authors
Keywords
Adolescent
Adult
Aged
Algorithms
Area Under Curve
Databases, Factual
Decision Support Techniques
Donor Selection
Female
Graft Survival
Humans
Liver Transplantation
Machine Learning
Male
Middle Aged
Patient Selection
Predictive Value of Tests
Reproducibility of Results
Risk Assessment
Risk Factors
ROC Curve
Time Factors
Tissue Donors
Treatment Failure
Victoria
Young Adult