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.

ICU 病人实验室测试的调度是一个重大挑战，先前研究基于病人信息使用离线强化学习方法为实验室测试定制最优策略，本研究在新发布的 MIMIC-IV 数据集上使用最新的离线强化学习方法评估和改进 ICU 病人实验室测试的调度策略。