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

2019-01-15 · Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-wei H. Lehman, Andrew Ross, Aldo Faisal, Finale Doshi-Velez

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 outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework to personalize sepsis treatment. The mixture model selectively alternates between neighbor-based (kernel) and deep reinforcement learning (DRL) experts depending on patient's current history. On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.

📄 PDF Abstract BibTeX arXiv:1901.04670

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Deep Reinforcement LearningMixture-of-Expertsreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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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…

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

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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

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TECM*: A Data-Driven Assessment to Reinforcement Learning Methods and Application to Heparin Treatment Strategy for Surgical Sepsis

2025-12-02 · Jiang Liu, Yujie Li, Chan Zhou, Yihao Xie 외 arxiv

Objective: Sepsis is a life-threatening condition caused by severe infection leading to acute organ dysfunction. This study proposes a data-driven metric and a continuous reward function to optimize personalized heparin …

Reinforcement Learning