Policy Optimization for Dynamic Heart Transplant Allocation
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Abstract (arXiv)
Heart transplantation is a viable path for patients suffering from advanced heart failure, but this lifesaving option is severely limited due to donor shortage. Although the current allocation policy was recently revised in 2018, a major concern is that it does not adequately take into account pretransplant and post-transplant mortality. In this paper, we take an important step toward addressing these deficiencies. To begin with, we use historical data from UNOS to develop a new simulator that enables us to evaluate and compare the performance of different policies. We then leverage our simulator to demonstrate that the status quo policy is considerably inferior to the myopic policy that matches incoming donors to the patient who maximizes the number of years gained by the transplant. Moreover, we develop improved policies that account for the dynamic nature of the allocation process through the use of potentials -- a measure of a patient's utility in future allocations that we introduce. We also show that batching together even a handful of donors -- which is a viable option for a certain type of donors -- further enhances performance. Our simulator also allows us to evaluate the effect of critical, and often unexplored, factors in the allocation, such as geographic proximity and the tendency to reject offers by the transplant centers.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 3 of its 29 references that are in the map. First posted on arXiv 13 Dec 2025.
Builds on
3 of 29 references.
References · 3
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Citing papers per year: 2026: 5.
5 papers.
- 2026Near-Optimal Dynamic Matching via Coarsening with Application to Heart Transplantationcited by 4
- 2026Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentivescited by 4
- 2026Learning Potentials for Dynamic Matching and Application to Heart Transplantationcited by 3
- 2026Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysiscited by 2
- 2026Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Valuescited by 1
- 2026Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values29 Jul 2026
- 2026Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis1 Jun 2026
- 2026Learning Potentials for Dynamic Matching and Application to Heart Transplantation9 Feb 2026
- 2026Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives4 Feb 2026
- 2026Near-Optimal Dynamic Matching via Coarsening with Application to Heart Transplantation4 Feb 2026