Summary of Measurement Scheduling For Icu Patients with Offline Reinforcement Learning, by Zongliang Ji et al.
Measurement Scheduling for ICU Patients with Offline Reinforcement Learning
by Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of scheduling laboratory tests for intensive care unit (ICU) patients. The authors highlight that 20-40% of lab tests ordered in the ICU are redundant, and previous work has applied offline reinforcement learning (Offline-RL) to optimize policies for ordering lab tests based on patient information. The study introduces a preprocessing pipeline for the MIMIC-IV dataset, designed specifically for time-series tasks, and evaluates state-of-the-art Offline-RL methods in identifying better policies for ICU patient lab test scheduling. The authors also discuss the practicality of using Offline-RL frameworks for scheduling laboratory tests in ICU settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to schedule lab tests for people in intensive care units (ICUs). Right now, 20-40% of these tests are unnecessary and don’t affect patient safety. Previous research has tried to find a better way to order lab tests using offline reinforcement learning (Offline-RL). In this study, the authors create a special tool for processing data from a new ICU dataset, called MIMIC-IV. They then test different Offline-RL methods to see which ones work best for scheduling lab tests in ICUs. |
Keywords
* Artificial intelligence * Reinforcement learning * Time series
