Deep Reinforcement Learning for Efficient and Fair Allocation of Health
  Care Resources
Yikuan Li Chengsheng Mao Kaixuan Huang Hanyin Wang Zheng Yu Mengdi Wang Yuan Luo
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.