Modelling Survival by Machine Learning Methods in Liver Transplantation: Application to the UNOS Dataset
The aim of this study is to develop and validate a machine learning (ML) model for predicting survival after liver transplantation based on pre-transplant donor and recipient characteristics. For this purpose, we consider a database from the United Network for Organ Sharing (UNOS), containing 29 variables and 39,095 donor-recipient pairs, describing liver transplantations performed in the United States of America from November 2004 until June 2015. The dataset contains more than a \(74\%\) of censoring, being a challenging and difficult problem. Several methods including proportional-hazards regression models and ML methods such as Gradient Boosting were applied, using 10 donor characteristics, 15 recipient characteristics and 4 shared variables associated with the donor-recipient pair. In order to measure the performance of the seven state-of-the-art methodologies, three different evaluation metrics are used, being the concordance index (ipcw) the most suitable for this problem. The results achieved show that, for each measure, a different technique obtains the highest value, performing almost the same, but, if we focus on ipcw, Gradient Boosting outperforms the rest of the methods.
KeywordsUnited Network for Organ Sharing Liver transplant Survival analysis Machine learning
This research has been partially supported by the Ministerio de Economía, Industria y Competitividad of Spain (Refs. TIN2017-90567-REDT and TIN2017-85887-C2-1-P). D. Guijo-Rubio’s research has been supported by the FPU Predoctoral Program from Spanish Ministry of Education and Science (Grant Ref. FPU16/02128).
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