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 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a4a85e94-21f7-4f27-85ad-540f5df9651f
- author
- Medved, Dennis
LU
; Nugues, Pierre
LU
and Nilsson, Johan
LU
- organization
-
- Department of Computer Science
- Artificial Intelligence in CardioThoracic Sciences (AICTS) (research group)
- Thoracic Surgery
- Heart and Lung transplantation (research group)
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
- eSSENCE: The e-Science Collaboration
- Robotics and Semantic Systems
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : Smarter Technology for a Healthier World, EMBC 2017 - Proceedings - Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
- series title
- Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
- article number
- 8036766
- pages
- 4 pages
- 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
- conference location
- SEOGWIPO City, Korea, Republic of
- conference dates
- 2017-07-11 - 2017-07-15
- external identifiers
-
- scopus:85032215444
- scopus:85032215444
- pmid:29059814
- ISSN
- 1557-170X
- ISBN
- 9781509028092
- 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
- 2024-05-26 20:17:47
@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 = {{2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : Smarter Technology for a Healthier World, EMBC 2017 - Proceedings}},
isbn = {{9781509028092}},
issn = {{1557-170X}},
language = {{eng}},
pages = {{74--77}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS}},
title = {{Predicting the Outcome for Patients in a Heart Transplantation Queue using Deep Learning}},
url = {{http://dx.doi.org/10.1109/EMBC.2017.8036766}},
doi = {{10.1109/EMBC.2017.8036766}},
year = {{2017}},
}