Simulating the Outcome of Heart Allocation Policies Using Deep Neural Networks

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 aft...

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Bibliographic Details
Main Authors: Medved, Dennis, Nugues, Pierre, Nilsson, Johan
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Heart
Measurement
Neural networks
Prediction algorithms
Predictive models
Resource management
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Summary: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.
ISSN:1558-4615
2694-0604
DOI:10.1109/EMBC.2018.8513637