Measurement Scheduling for ICU Patients with Offline Reinforcement Learning
This work addresses the challenge of reducing unnecessary lab tests in ICU settings, which is incremental as it applies existing offline-RL methods to new data.
The study tackled the problem of scheduling laboratory tests for ICU patients, where 20-40% of tests are redundant, by applying offline reinforcement learning to the MIMIC-IV dataset, resulting in an exploration of state-of-the-art methods to identify better policies.
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.