Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care.

ABSTRACT:

Background

Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically 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.

Methods

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, an optimal oxygen control policy is learned by using deep deterministic policy gradient (DDPG) and real-time recommends the oxygen flo

SUBMITTER: Zheng H 

PROVIDER: S-EPMC8142656 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature