Simulating the Outcome of Heart Allocation Policies Using Deep Neural Networks
Medved, Dennis LU
; Nugues, Pierre
LU
and Nilsson, Johan
LU
(2018)
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Abstract
- We created a system to simulate the heart allocation process in a transplant queue, using a discrete event model and a neural network algorithm, which we named the Lund Deep Learning Transplant Algorithm (LuDeLTA). LuDeLTA is utilized to predict the survival of the patients both in the queue and after transplant. We tried four different allocation policies: wait time, clinical rules and allocating the patients using either LuDeLTA or The International Heart Transplant Survival Algorithm (IHTSA) model. Both IHTSA and LuDeLTA were used to evaluate the results. The predicted mean survival for allocating according to wait time was about 4,300 days, clinical rules 4,300 days and using neural networks 4,700 days.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c9075310-7d67-4dbf-ab9e-c8ef1d7c5e0c
- author
- Medved, Dennis
LU
; Nugues, Pierre
LU
and Nilsson, Johan
LU
- organization
-
- Department of Computer Science
- Artificial Intelligence in CardioThoracic Sciences (AICTS) (research group)
- eSSENCE: The e-Science Collaboration
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
- Heart and Lung transplantation (research group)
- Thoracic Surgery
- Robotics and Semantic Systems
- publishing date
- 2018
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- conference location
- Honolulu, United States
- conference dates
- 2018-07-18 - 2018-07-21
- external identifiers
-
- pmid:30441736
- scopus:85056626507
- ISBN
- 978-1-5386-3647-3
- 978-1-5386-3646-6
- DOI
- 10.1109/EMBC.2018.8513637
- language
- English
- LU publication?
- yes
- id
- c9075310-7d67-4dbf-ab9e-c8ef1d7c5e0c
- alternative location
- https://ieeexplore.ieee.org/document/8513637
- date added to LUP
- 2018-08-10 10:29:13
- date last changed
- 2024-03-18 12:52:56
@inproceedings{c9075310-7d67-4dbf-ab9e-c8ef1d7c5e0c,
abstract = {{We created a system to simulate the heart allocation process in a transplant queue, using a discrete event model and a neural network algorithm, which we named the Lund Deep Learning Transplant Algorithm (LuDeLTA). LuDeLTA is utilized to predict the survival of the patients both in the queue and after transplant. We tried four different allocation policies: wait time, clinical rules and allocating the patients using either LuDeLTA or The International Heart Transplant Survival Algorithm (IHTSA) model. Both IHTSA and LuDeLTA were used to evaluate the results. The predicted mean survival for allocating according to wait time was about 4,300 days, clinical rules 4,300 days and using neural networks 4,700 days.}},
author = {{Medved, Dennis and Nugues, Pierre and Nilsson, Johan}},
booktitle = {{Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}},
isbn = {{978-1-5386-3647-3}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Simulating the Outcome of Heart Allocation Policies Using Deep Neural Networks}},
url = {{http://dx.doi.org/10.1109/EMBC.2018.8513637}},
doi = {{10.1109/EMBC.2018.8513637}},
year = {{2018}},
}