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

Dennis Medved1, Johan Nilsson2, Pierre Nugues1

  • 1Lund University
  • 2Dept. Clinical Sciences Lund, CardioThoracic Surgery, Lund University, Lund



Contributed Papers (Oral)


4. Computational Systems & Synthetic Biology; Multiscale Modeling


08:00 - 09:30 | Wed 12 Jul | Schmitt Room | WeAT10

Models for Clinical Decision Support


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.

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