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

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

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 currently used treatment strategies are problematic and may cause harm to patients. To address this decision problem, we propose a new medical decision model based on historical data to help clinicians recommend the best reference option for real-time treatment. Our model combines offline reinforcement learning and deep reinforcement learning to solve the problem of traditional reinforcement learning in the medical field due to the inability to interact with the environment, while enabling our model to make decisions in a continuous state-action space. We demonstrate that, on average, the treatments recommended by the model are more valuable and reliable than those recommended by clinicians. In a large validation dataset, we find out that the patients whose actual doses from clinicians matched the decisions made by AI has the lowest mortality rates. Our model provides personalized and clinically interpretable treatment decisions for sepsis to improve patient care.

📄 PDF Abstract BibTeX arXiv:2206.11190

Code (0)

등록된 구현이 없습니다.

Tasks

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

A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies

2022-03-25 · Pramod Kaushik, Sneha Kummetha, Perusha Moodley, Raju S. Bapi

Sepsis is a leading cause of mortality and its treatment is very expensive. Sepsis treatment is also very challenging because there is no consensus on what interventions work best and different patients respond very diff…

Deep Reinforcement Learning Offline RL Q-Learning

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)

Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications

2023-02-15 · Milashini Nambiar, Supriyo Ghosh, Priscilla Ong, Yu En Chan 외

There is increasing interest in data-driven approaches for recommending optimal treatment strategies in many chronic disease management and critical care applications. Reinforcement learning methods are well-suited to th…

Decision Making Management Offline RL Q-Learning+4

MORE-CLEAR: Multimodal Offline Reinforcement learning for Clinical notes Leveraged Enhanced State Representation

2025-08-11 · Yooseok Lim, ByoungJun Jeon, Seong-A Park, Jisoo Lee 외 arxiv

Sepsis, a life-threatening inflammatory response to infection, causes organ dysfunction, making early detection and optimal management critical. Previous reinforcement learning (RL) approaches to sepsis management rely p…

Reinforcement Learning Offline 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