Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes. (arXiv:2205.09852v1 [cs.LG])

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 …

actor-critic arxiv network policy treatment