ICU-Sepsis: A Benchmark MDP Built from Real Medical Data
This provides a standardized tool for RL researchers to benchmark algorithms on a complex real-world medical problem, though it is incremental as it builds on existing sepsis modeling efforts.
The authors tackled the challenge of creating a usable benchmark for evaluating reinforcement learning algorithms in sepsis management by introducing ICU-Sepsis, a lightweight tabular MDP environment built from real medical data, which is designed to be challenging for state-of-the-art methods.
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.