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

Medved, Dennis LU ; Nugues, Pierre LU and Nilsson, Johan LU (2017) 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 In Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) p.74-77
Abstract
Heart transplantations have made it possible to extend the median survival time 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... (More)
Heart transplantations have made it possible to extend the median survival time 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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
pages
74 - 77
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
external identifiers
  • scopus:85032215444
DOI
10.1109/EMBC.2017.8036766
language
English
LU publication?
yes
id
a4a85e94-21f7-4f27-85ad-540f5df9651f
date added to LUP
2017-08-09 11:22:23
date last changed
2018-03-12 21:00:08
@inproceedings{a4a85e94-21f7-4f27-85ad-540f5df9651f,
  abstract     = {Heart transplantations have made it possible to extend the median survival time 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.},
  author       = {Medved, Dennis and Nugues, Pierre and Nilsson, Johan},
  booktitle    = {Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  language     = {eng},
  pages        = {74--77},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Predicting the Outcome for Patients in a Heart Transplantation Queue using Deep Learning},
  url          = {http://dx.doi.org/10.1109/EMBC.2017.8036766},
  year         = {2017},
}