ICU-Sepsis: A Benchmark MDP Built from Real Medical Data

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.

Accepted at the Reinforcement Learning Conference, 2024.

Paper | Code

Cite as:

@inproceedings{
  choudhary2024icusepsis,
  title={{ICU-Sepsis}: A Benchmark {MDP} Built from Real Medical Data},
  author={Kartik Choudhary and Dhawal Gupta and Philip S. Thomas},
  booktitle={Reinforcement Learning Conference},
  year={2024},
  url={https://arxiv.org/abs/2406.05646}
}