ICU-Sepsis: A Benchmark MDP Built from Real Medical Data
Choudhary, Kartik, Gupta, Dhawal, Thomas, Philip S.
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Jun-9-2024
- Country:
- Europe > United Kingdom
- England > Cambridgeshire > Cambridge (0.14)
- North America > United States
- New York > New York County > New York City (0.14)
- Europe > United Kingdom
- Genre:
- Research Report > Experimental Study (0.93)
- Industry:
- Technology: