# Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources

arXiv: https://arxiv.org/abs/2309.08560 · HTML: https://mlsanity.com/paper/2309.08560/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction](https://mlsanity.com/paper/2007.10306/) (2020)
- [VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning](https://mlsanity.com/paper/2009.06602/) (2020)
- [Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage](https://mlsanity.com/paper/2110.10994/) (2021)
- [Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration](https://mlsanity.com/paper/2301.12822/) (2023)

## Built on by (most cited first, 1 of 1)

- [Situational-Constrained Sequential Resources Allocation via Reinforcement Learning](https://mlsanity.com/paper/2506.14125/) (2025)

## Abstract (arXiv)

Scarcity of health care resources could result in the unavoidable consequence of rationing. For example, ventilators are often limited in supply, especially during public health emergencies or in resource-constrained health care settings, such as amid the pandemic of COVID-19. Currently, there is no universally accepted standard for health care resource allocation protocols, resulting in different governments prioritizing patients based on various criteria and heuristic-based protocols. In this study, we investigate the use of reinforcement learning for critical care resource allocation policy optimization to fairly and effectively ration resources. We propose a transformer-based deep Q-network to integrate the disease progression of individual patients and the interaction effects among patients during the critical care resource allocation. We aim to improve both fairness of allocation and overall patient outcomes. Our experiments demonstrate that our method significantly reduces excess deaths and achieves a more equitable distribution under different levels of ventilator shortage, when compared to existing severity-based and comorbidity-based methods in use by different governments. Our source code is included in the supplement and will be released on Github upon publication.