  Patients with severe Coronavirus disease 19 (COVID-19) typically require
supplemental oxygen as an essential treatment. We developed a machine learning
algorithm, based on a deep Reinforcement Learning (RL), for continuous
management of oxygen flow rate for critical ill patients under intensive care,
which can identify the optimal personalized oxygen flow rate with strong
potentials to reduce mortality rate relative to the current clinical practice.
Basically, we modeled the oxygen flow trajectory of COVID-19 patients and their
health outcomes as a Markov decision process. Based on individual patient
characteristics and health status, a reinforcement learning based oxygen
control policy is learned and real-time recommends the oxygen flow rate to
reduce the mortality rate. We assessed the performance of proposed methods
through cross validation by using a retrospective cohort of 1,372 critically
ill patients with COVID-19 from New York University Langone Health ambulatory
care with electronic health records from April 2020 to January 2021. The mean
mortality rate under the RL algorithm is lower than standard of care by 2.57%
(95% CI: 2.08- 3.06) reduction (P<0.001) from 7.94% under the standard of care
to 5.37 % under our algorithm and the averaged recommended oxygen flow rate is
1.28 L/min (95% CI: 1.14-1.42) lower than the rate actually delivered to
patients. Thus, the RL algorithm could potentially lead to better intensive
care treatment that can reduce mortality rate, while saving the oxygen scarce
resources. It can reduce the oxygen shortage issue and improve public health
during the COVID-19 pandemic.
