mlsanityData to 14 Oct 2026

Policy Optimization for Dynamic Heart Transplant Allocation

arXiv 2512.12497 ↗First posted 13 Dec 2025cs.LGcited by 5

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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

  1. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  2. 2016Personalized Donor-Recipient Matching for Organ Transplantationcited by 4
  3. 2021Learning Matching Representations for Individualized Organ Transplantation Allocationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.