Predicting the Outcome for Patients in a Heart Transplantation Queue using Deep Learning

Dennis Medved • Johan Nilsson • Pierre Nugues

08:00 - 08:15 | Wednesday 12 July 2017 | Schmitt Room


Heart transplantation has enabled to extend the median survival rate to 12 years for patients with end-stage heart diseases. This operation is unfortunately limited by the availability of donor organs and patients have to wait on average about 200 days in a waiting list before being operated. This waiting time varies considerably across the patients. In this paper, we studied the outcome for patients entering a transplantation waiting list using deep learning techniques. We implemented a model in the form of two-layer neural networks and we predicted the outcome as: still waiting, transplanted, or dead in the waiting list at three different time points: 180 days, 365 days, and 730 days. As data source, we used the United Network for Organ Sharing (UNOS) registry, where we extracted adult patients (> 17 years) from January 2000 to December 2011. We trained our model using the Keras framework and we report F1 macro scores of respectively 0.674, 0.680, and 0.680 compared to a baseline of 0.271. We also applied a backward elimination procedure, using our neural network, to extract the 10 most significant parameters predicting the patient status for the three different time points.