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Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning

arXiv AI Archived May 25, 2026 ✓ Full text saved

arXiv:2605.23320v1 Announce Type: new Abstract: Ventilator decision support requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and clinician specific tuning styles. Rule based approaches rarely generalize personalization, and end to end reinforcement learning or single large language model systems remain difficult to control and audit. We propose the Ventilator Decision Support System (VDSS), a human in the loop multi agent fr

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    Computer Science > Artificial Intelligence [Submitted on 22 May 2026] Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning Sijia Li, Xiaoyu Tan, Qixing Wang, Weiyi Zhao, Chen Zhan, Teqi Hao, Xuemin Wang, Lei Gu, Roland Eils, Xihe Qiu Ventilator decision support requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and clinician specific tuning styles. Rule based approaches rarely generalize personalization, and end to end reinforcement learning or single large language model systems remain difficult to control and audit. We propose the Ventilator Decision Support System (VDSS), a human in the loop multi agent framework that coordinates modular decision components through contract driven structured interfaces and produces traceable evidence for review. VDSS performs online preference adaptation with a contextual bandit, updating clinician specific preferences from the final accepted decision at each adjustment cycle and using them to guide subsequent recommendations. Structured rejection feedback triggers targeted replanning to reduce unproductive iterations and improve interaction stability. Retrospective ICU trajectory replay with expert review indicates higher recommendation acceptability and fewer interaction rounds to reach an acceptable plan, supporting clinically deployable human AI collaboration. Comments: miccai 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.23320 [cs.AI]   (or arXiv:2605.23320v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.23320 Focus to learn more Submission history From: Sijia Li [view email] [v1] Fri, 22 May 2026 07:36:26 UTC (8,421 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    May 25, 2026
    Archived
    May 25, 2026
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