Deep Reinforcement Learning for Sepsis Treatment

2017-11-27 · Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, Marzyeh Ghassemi

Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. The learned policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.

📄 PDF Abstract BibTeX arXiv:1711.09602

Code (2)

darkefyre/sepsisrl 공식 구현

uribyul/py_ai_clinician

Tasks

Decision MakingDeep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)State Space Models

Model-Based Reinforcement Learning for Sepsis Treatment

2018-11-23 · Aniruddh Raghu, Matthieu Komorowski, Sumeetpal Singh

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…

model Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Offline reinforcement learning with uncertainty for treatment strategies in sepsis

2021-07-09 · Ran Liu, Joseph L. Greenstein, James C. Fackler, Jules Bergmann 외

Guideline-based treatment for sepsis and septic shock is difficult because sepsis is a disparate range of life-threatening organ dysfunctions whose pathophysiology is not fully understood. Early intervention in sepsis is…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning

2019-01-15 · Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman 외

Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best…

Deep Reinforcement Learning Mixture-of-Experts reinforcement-learning Reinforcement Learning+1

Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space

2022-06-22 · Zeyu Wang, Huiying Zhao, Peng Ren, Yuxi Zhou 외

Sepsis is a leading cause of death in the ICU. It is a disease requiring complex interventions in a short period of time, but its optimal treatment strategy remains uncertain. Evidence suggests that the practices of curr…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis

2018-02-08 · Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell 외

Sepsis is a life-threatening condition affecting one million people per year in the US in which dysregulation of the body's own immune system causes damage to its tissues, resulting in a 28 - 50% mortality rate. Clinical…