A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis.

ABSTRACT: Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms cannot evaluate the target value function precisely and are not as safe as clinical experts. In this study, we propose a Weighted Dueling Double Deep Q-Network with embedded human Expertise (WD3QNE). A target Q value function with adaptive dynamic weight is designed to improve the estimate accuracy and human expertise in decision-making is leveraged. In addition, the random forest algorithm is employed for feature selection to improve model interpretability. We test our algorithm against state-of-the-art value function methods in terms of e

SUBMITTER: Wu X 

PROVIDER: S-EPMC9894526 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature