  Scheduling laboratory tests for ICU patients presents a significant
challenge. Studies show that 20-40% of lab tests ordered in the ICU are
redundant and could be eliminated without compromising patient safety. Prior
work has leveraged offline reinforcement learning (Offline-RL) to find optimal
policies for ordering lab tests based on patient information. However, new ICU
patient datasets have since been released, and various advancements have been
made in Offline-RL methods. In this study, we first introduce a preprocessing
pipeline for the newly-released MIMIC-IV dataset geared toward time-series
tasks. We then explore the efficacy of state-of-the-art Offline-RL methods in
identifying better policies for ICU patient lab test scheduling. Besides
assessing methodological performance, we also discuss the overall suitability
and practicality of using Offline-RL frameworks for scheduling laboratory tests
in ICU settings.
