mlsanityData to 7 Oct 2026

Measurement Scheduling for ICU Patients with Offline Reinforcement Learning

arXiv 2402.07344 ↗First posted 12 Feb 2024cs.LG cs.AIcited by 2

On the map: Offline RL and imitation learning from data → Safe offline RL

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 8 of its 21 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

8 of 21 references.

References · 8

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  3. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
  4. 2015Doubly Robust Off-policy Value Evaluation for Reinforcement Learningcited by 337
  5. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  6. 2017A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Unitscited by 50
  7. 2018An Optimal Policy for Patient Laboratory Tests in Intensive Care Unitscited by 7
  8. 2019Dynamic Measurement Scheduling for Event Forecasting using Deep RLcited by 7

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025The Curious Language Model: Strategic Test-Time Information Acquisitioncited by 5
  2. 2025ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orderscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Offline Reinforcement Learning for Hemodynamic Management of Sepsis in the ICU: a MIMIC-IV Study with Dual Off-Policy Evaluation
  2. 2026Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning
  3. 2022Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
  4. 2021Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings
  5. 2025Benchmarking Offline Multi-Objective Reinforcement Learning in Critical Care
  6. 2023Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
  7. 2025Advancing Safe Mechanical Ventilation Using Offline RL With Hybrid Actions and Clinically Aligned Rewards
  8. 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination