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 differently to the same treatment. Deep Reinforcement Learning methods can be used to come up with optimal policies for treatment strategies mirroring physician actions. In the healthcare scenario, the available data is mostly collected offline with no interaction with the environment, which necessitates the use of offline RL techniques. The Offline RL paradigm suffers from action distribution shifts which in turn negatively affects learning an optimal policy for the treatment. In this work, a Conservative-Q Learning (CQL) algorithm is used to mitigate this shift and its corresponding policy reaches closer to the physicians policy than conventional deep Q Learning. The policy learned could help clinicians in Intensive Care Units to make better decisions while treating septic patients and improve survival rate.

📄 PDF Abstract BibTeX arXiv:2203.13884

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement LearningOffline RLQ-Learning

Uniformly Conservative Exploration in Reinforcement Learning

2021-10-25 · Wanqiao Xu, Jason Yecheng Ma, Kan Xu, Hamsa Bastani 외

A key challenge to deploying reinforcement learning in practice is avoiding excessive (harmful) exploration in individual episodes. We propose a natural constraint on exploration -- \textit{uniformly} outperforming a con…

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

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

Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment

2026-01-20 · Punit Kumar, Vaibhav Saran, Divyesh Patel, Nitin Kulkarni 외 arxiv

Sepsis remains one of the leading causes of mortality in intensive care units, where timely and accurate treatment decisions can significantly impact patient outcomes. In this work, we propose an interpretable decision s…

Reinforcement Learning Data Augmentation

Sepsis World Model: A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment

2019-12-15 · Amirhossein Kiani, Chris Wang, Angela Xu

Sepsis is a life-threatening condition caused by the body's response to an infection. In order to treat patients with sepsis, physicians must control varying dosages of various antibiotics, fluids, and vasopressors based…

model OpenAI Gym 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)