Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control

Reinforcement learning (RL) applications in Clinical Decision Support Systems (CDSS) frequently encounter skepticism from practitioners regarding inoperable dosing decisions. We address this challenge with an end-to-end approach for learning optimal drug dosing and control policies for dual vasopressor administration in intensive care unit (ICU) patients with septic shock. For realistic drug dosing, we apply action space design that accommodates discrete, continuous, and directional dosing strategies in a system that combines offline conservative Q-learning with a novel recurrent modeling in a replay buffer to capture temporal dependencies in ICU time-series data. Our comparative analysis of norepinephrine dosing strategies across different action space formulations reveals that the designed action spaces improve interpretability and facilitate clinical adoption while preserving efficacy. Empirical results1 on eICU and MIMIC demonstrate that action space design profoundly influences learned behavioral policies. The proposed methods achieve improved patient outcomes of over 15% in survival improvement probability, while aligning with established clinical protocols.

RISE: Robust Individualized Decision Learning with Sensitive Variables

This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the intervention decision, but their inclusion in decision making is prohibited due to reasons such as delayed availability or fairness concerns. A naive baseline is to ignore these sensitive variables in learning decision rules, leading to significant uncertainty and bias. To address this, we propose a decision learning framework to incorporate sensitive variables during offline training but not include them in the input of the learned decision rule during model deployment. Specifically, from a causal perspective, the proposed framework intends to improve the worst-case outcomes of individuals caused by sensitive variables that are unavailable at the time of decision. Unlike most existing literature that uses mean-optimal objectives, we propose a robust learning framework by finding a newly defined quantile- or infimum-optimal decision rule. The reliable performance of the proposed method is demonstrated through synthetic experiments and three real-world applications.