title: Selection of an optimal feature set to predict heart transplantation outcomes.
abstract: Heart transplantation (HT) is a life saving procedure, but a limited donor supply forces the surgeons to prioritize the recipients. The understanding of factors that predict mortality could help the doctors with this task. The objective of this study is to find locally optimal feature sets to predict survival of HT patients for different time periods. To this end, we applied logistic regression together with a greedy forward and backward search. As data source, we used the United Network for Organ Sharing (UNOS) registry, where we extracted adult patients (>17 years) from January 1997 to December 2008. As methods to predict survival, we used the Index for Mortality Prediction After Cardiac Transplantation (IMPACT) and the International Heart Transplant Survival Algorithm (IHTSA). We used the LIBLINEAR library together with the Apache Spark cluster computing framework to carry out the computation and we found feature sets for 1, 5, and 10 year survival for which we obtained area under the ROC curves (AUROC) of 68%, 68%, and 76%, respectively.
authors.forename: Dennis
authors.initials: D
authors.lastname: Medved
authors.forename: Pierre
authors.initials: P
authors.lastname: Nugues
authors.forename: Johan
authors.initials: J
authors.lastname: Nilsson
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D015331:Cohort Studies; D016027:Heart Transplantation; D006801:Humans; D016015:Logistic Models; D012372:ROC Curve; D012042:Registries; D015203:Reproducibility of Results; D014019:Tissue Donors; D016896:Treatment Outcome