Multi-Task Survival Analysis of Liver Transplantation Using Deep Learning

In this paper, we present the application of deep learning techniques to develop a modern model for the prediction of graft failure and survival analysis in liver transplant patients. We trained our model using the United Network for Organ Sharing (UNOS) dataset consisting of 59,115 patients from year 2002 to 2016 with around 150 features each. We also compare our model against an- other dataset – Scientific Registry of Transplant Recipients (SRTR) including 87,334 patients from year 2002 to 2018 – after selecting features by mapping them from UNOS data. Some of the most important features common to both datasets are Model for End-stage Liver Disease (MELD) score, patient body mass index (BMI), donor and patient age, cold ischemia time, and levels of various chemicals within the patient. To provide an additional tool to clinical practitioners in the allocation of a scarce resource, we developed a multi-task model to learn the survival function of a donor-recipient pair and hence predict the exact time of failure which outper- forms the traditional cox hazard models. The multi-task model produces very promising C-index results of 0.82 and 0.57 on the SRTR and UNOS datasets respectively.