ICU-Sepsis: A Benchmark MDP Built from Real Medical Data
Keywords: Evaluation, Applied RL, Multidisciplinary work
TL;DR: ICU-Sepsis -- A tabular benchmark environment for RL algorithms build with real medical data that is lightweight and easy-to-use yet challenging to most algorithms.
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.
Submission Number: 194
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