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.
Code (1)
Tasks
BenchmarkingManagementReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening syst…
Decision MakingModel-Based Reinforcement Learning for Sepsis Treatment
Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universal…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Explainable AI For Early Detection Of Sepsis
Sepsis is a life-threatening condition that requires rapid detection and treatment to prevent progression to severe sepsis, septic shock, or multi-organ failure. Despite advances in medical technology, it remains a major…
Clinical KnowledgeThe Evolving Definition of Sepsis
Sepsis affects millions of people worldwide each year. It occurs when a normal human immune response to a bacterial, viral or fungal infection becomes dysfunctional and triggers widespread inflammation that results in se…
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern …
BIG-bench Machine LearningInterpretable Machine Learning