Measurement Scheduling for ICU Patients with Offline Reinforcement Learning
<|reference_start|>Measurement Scheduling for ICU Patients with Offline Reinforcement Learning: 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.<|reference_end|>
@article{ji2024measurement,
    title={Measurement Scheduling for ICU Patients with Offline Reinforcement
  Learning},
    author={Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan},
    journal={arXiv preprint arXiv:2402.07344},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07344},
    primaryClass={cs.LG cs.AI}
}