Measurement Scheduling for ICU Patients with Offline Reinforcement
Learning
- URL: http://arxiv.org/abs/2402.07344v1
- Date: Mon, 12 Feb 2024 00:22:47 GMT
- Title: Measurement Scheduling for ICU Patients with Offline Reinforcement
Learning
- Authors: Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan
- Abstract summary: 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.
New ICU patient datasets have since been released, and various advancements have been made in Offline-RL methods.
- Score: 16.07235754244993
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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