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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:
author
organization
publishing date
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
  • 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
2019-02-20 11:23:56
@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},
  isbn         = { 978-1-5386-3647-3 },
  language     = {eng},
  location     = {Honolulu, United States},
  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},
  year         = {2018},
}