让呼吸机决策更懂医生：用多智能体系统动态学习医生偏好，提升协作效率。
Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning
- 多智能体框架通过合约接口协同，可追踪并记录每步决策依据。
- 基于上下文老虎机算法在线学习医生偏好，减少无效交互轮次。
- 支持医生反馈触发精准重规划，适合临床部署的人机协作场景。
呼吸机决策支持需在动态生理变化和疾病进展中做出序列化决策，同时遵守安全边界并适应不同医生的调参风格。传统规则方法难以实现个性化，而端到端强化学习或单一大语言模型系统又难于控制与审计。本文提出呼吸机决策支持系统（VDSS），一个以人为核心的多智能体框架，通过契约式结构化接口协调模块化决策组件，并生成可追溯的证据供审查。VDSS采用上下文老虎机算法进行在线偏好自适应，在每次调整周期后根据最终采纳的决策更新医生偏好，并用于指导后续推荐。结构化拒绝反馈可触发针对性重规划，减少无意义迭代，提升交互稳定性。通过专家评审的回溯式ICU病程重演验证，该系统显著提升了推荐接受率，并减少达到可接受方案所需的交互轮次，支持临床可用的人机协同。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。