Inferring Continuous Treatment Doses from Historical Data via Model-Based Entropy-Regularized Reinforcement Learning

摘要

Developments in Reinforcement Learning and the availability of healthcare data sources such as Electronic Health Records (EHR) provide an opportunity to derive data-driven treatment dose recommendations for patients and improve clinical outcomes. Recent studies have focused on deriving discretized dosages using offline historical data extracted from EHR. In this paper, we propose an Actor-Critic framework to infer continuous dosage for treatment recommendation and demonstrate its advantage in numerical stability as well as interpretability. In addition, we incorporate a Bayesian Neural Network as a simulation model and probability-based regularization techniques to alleviate the distribution shift in off-line learning environments to increase practical safety. Experiments on a real-world EHR data set, MIMIC-III, show that our approach can achieve improved performance while maintaining similarity to expert clinician treatments in comparison to other baseline methods.