Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.

Abstract

Blood pressure variability (BPV) plays a critical role in vascular diseases, particularly in acute ischemic stroke patients in intensive care units (ICUs), where higher BPV correlates with increased mortality rates. Current interventions lack effective methods for controlling BPV across consecutive time windows. To addressing this gap, we propose an offline deep reinforcement learning approach with supervised guidance to regulate systolic BPV in the following consecutive time windows by optimizing intravenous nicardipine infusion rates for intracerebral hemorrhage patients. Using clinically inspired reward functions, our method aims to tailor antihypertensive medication management within the critical 24-hour recovery window. Compared to human performance, our best method showed 57.52% and 126.01% improvements over the human baseline for maintaining BP within the desired range for the next time window and across two consecutive time windows. This research promises streamlined antihypertensive medication dosing, offering potential just-in-time adaptive interventions through automated pumps during stroke patients' ICU stays.