Simulating the Outcome of Heart Allocation Policies Using Deep Neural Networks

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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.

Detaljer

Författare
Enheter & grupper
Originalspråkengelska
Titel på värdpublikationProceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (elektroniskt)978-1-5386-3646-6
ISBN (tryckt) 978-1-5386-3647-3
StatusPublished - 2018
PublikationskategoriForskning
Peer review utfördJa
Evenemang40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, USA
Varaktighet: 2018 jul 182018 jul 21

Konferens

Konferens40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
LandUSA
OrtHonolulu
Period2018/07/182018/07/21