2024/02/12 by Zongliang Ji, Ji, Zongliang, Anna Goldenberg +3 · 1 citation
Medicine · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Healthcare Technology and Patient Monitoring #Intensive Care Unit Cognitive Disorders #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2402.07344
openalex publication_date 2024/02/12 · openalex created_date 2024/02/14 · openalex updated_date 2026/07/28
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.