Learning Potentials for Dynamic Matching and Application to Heart Transplantation

• URL: http://arxiv.org/abs/2602.08878v1
• Date: Mon, 09 Feb 2026 16:39:12 GMT
• Title: Learning Potentials for Dynamic Matching and Application to Heart Transplantation
• Authors: Itai Zilberstein, Ioannis Anagnostides, Zachary W. Sollie, Arman Kilic, Tuomas Sandholm,
• Abstract summary: We propose a novel framework for non-myopic policy optimization in general online matching relying on potentials.<n>Our approach is a form of self-supervised imitation learning: the potentials are trained to mimic an algorithm that has perfect foresight.
• Score: 45.83272225462161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Each year, thousands of patients in need of heart transplants face life-threatening wait times due to organ scarcity. While allocation policies aim to maximize population-level outcomes, current approaches often fail to account for the dynamic arrival of organs and the composition of waitlisted candidates, thereby hampering efficiency. The United States is transitioning from rigid, rule-based allocation to more flexible data-driven models. In this paper, we propose a novel framework for non-myopic policy optimization in general online matching relying on potentials, a concept originally introduced for kidney exchange. We develop scalable and accurate ways of learning potentials that are higher-dimensional and more expressive than prior approaches. Our approach is a form of self-supervised imitation learning: the potentials are trained to mimic an omniscient algorithm that has perfect foresight. We focus on the application of heart transplant allocation and demonstrate, using real historical data, that our policies significantly outperform prior approaches -- including the current US status quo policy and the proposed continuous distribution framework -- in optimizing for population-level outcomes. Our analysis and methods come at a pivotal moment in US policy, as the current heart transplant allocation system is under review. We propose a scalable and theoretically grounded path toward more effective organ allocation.

Related papers

• Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives [45.83272225462161]
  The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare.<n>Current approaches often overlook a fundamental barrier: incentives.<n>We argue that organ allocation is not merely an optimization problem, but rather a complex game involving organ procurement organizations, transplant centers, clinicians, patients, and regulators.
  arXiv  Detail & Related papers  (2026-02-04T19:24:06Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [86.99017195607077]
  We address real-time sampling and estimation of autoregressive Markovian sources in wireless networks.<n>We propose a graphical reinforcement learning framework for policy optimization.<n>Theoretically, our proposed policies are transferable, allowing a policy trained on one graph to be effectively applied to structurally similar graphs.
  arXiv  Detail & Related papers  (2026-01-19T02:18:45Z)
• Policy Optimization for Dynamic Heart Transplant Allocation [48.56507763517103]
  Heart transplantation is a viable path for patients suffering from advanced heart failure.<n>The current allocation policy does not adequately take into account pretransplant and post-transplant mortality.<n>We develop a new simulator that enables us to evaluate and compare the performance of different policies.
  arXiv  Detail & Related papers  (2025-12-13T23:51:31Z)
• Towards Efficient Prompt-based Continual Learning in Distributed Medical AI [0.13265175299265505]
  Modern AI models achieve state-of-the-art performance with large-scale, high-quality datasets.<n>Ethical, social, and institutional constraints in the medical domain severely restrict data sharing.<n>We propose a prompt-based continual learning (PCL) approach featuring a unified prompt pool with a minimal expansion strategy.
  arXiv  Detail & Related papers  (2025-08-14T06:46:14Z)
• Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care [46.2482873419289]
  We introduce a deep Q-learning approach to obtain more reliable critical care policies. We evaluate our method in off-policy and offline settings using simulated environments and real health records from intensive care units.
  arXiv  Detail & Related papers  (2023-06-13T18:02:57Z)
• Policy Optimization for Personalized Interventions in Behavioral Health [8.10897203067601]
  Behavioral health interventions, delivered through digital platforms, have the potential to significantly improve health outcomes. We study the problem of optimizing personalized interventions for patients to maximize a long-term outcome. We present a new approach for this problem that we dub DecompPI, which decomposes the state space for a system of patients to the individual level.
  arXiv  Detail & Related papers  (2023-03-21T21:42:03Z)
• Federated Offline Reinforcement Learning [55.326673977320574]
  We propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. We design the first federated policy optimization algorithm for offline RL with sample complexity. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed.
  arXiv  Detail & Related papers  (2022-06-11T18:03:26Z)
• Deep Normed Embeddings for Patient Representation [0.1310865248866973]
  We introduce a novel contrastive representation learning objective and a training scheme for clinical time series. We show how the learned embedding can be used for online patient monitoring, supplement clinicians and improve performance of downstream machine learning tasks.
  arXiv  Detail & Related papers  (2022-04-12T02:02:01Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.