Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes.

ABSTRACT: Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on electronic health records (EHR) has attracted interest from both the healthcare industry and machine learning research community. However, most learned DTR policies might be biased due to the existence of confounders. Although some treatment actions non-survivors received may be helpful, if confounders cause the mortality, the training of RL models guided by long-term outcomes (e.g., 90-day mortality) would punish those treatment actions causing the learned DTR policies to be suboptimal. In this study, we develop a new deconfounding actor-critic network (DAC) to lear

SUBMITTER: Yin C 

PROVIDER: S-EPMC9466407 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature