ICU-Sepsis: A Benchmark MDP Built from Real Medical Data
; 4:1546−1566, 2024.
Presented at the Reinforcement Learning Conference (RLC), Amherst Massachusetts, August 9–12, 2024.
Abstract
We present *ICU-Sepsis*, an environment that can be used in benchmarks for evaluating reinforcement learning (RL) algorithms. Sepsis management is a complex task that has been an important topic in applied RL research in recent years. Therefore, MDPs that model sepsis management can serve as part of a benchmark to evaluate RL algorithms on a challenging real-world problem. However, creating usable MDPs that simulate sepsis care in the ICU remains a challenge due to the complexities involved in acquiring and processing patient data. ICU-Sepsis is a lightweight environment that models personalized care of sepsis patients in the ICU. The environment is a tabular MDP that is widely compatible and is challenging even for state-of-the-art RL algorithms, making it a valuable tool for benchmarking their performance. However, we emphasize that while ICU-Sepsis provides a standardized environment for evaluating RL algorithms, it should not be used to draw conclusions that guide medical practice.
BibTeX
@article{choudhary2024sepsis,
title={{ICU-Sepsis}: {A} Benchmark {MDP} Built from Real Medical Data},
author={Choudhary, Kartik and Gupta, Dhawal and Thomas, Philip S.},
journal={Reinforcement Learning Journal},
volume={4},
pages={1546--1566},
year={2024}
}