用强化学习优化护士与患者匹配，兼顾技能、疲劳和位置因素。
NurseSchedRL: Attention-Guided Reinforcement Learning for Nurse-Patient Assignment
- 结合注意力机制建模护士技能、疲劳和位置，动态调整分配策略。
- 在模拟中提升调度效率，减少疲劳，更精准匹配护士与患者需求。
- 适合需要高效排班的医院管理者或医疗系统优化研究者。
医疗系统面临日益增长的压力，需在有限护理资源下，高效分配护士，同时考虑技能差异、患者病情严重程度、员工疲劳和照护连续性。传统优化与启发式排班方法难以应对这种多约束、动态变化的环境。本文提出 NurseSchedRL，一种基于强化学习的护士-患者分配框架，融合结构化状态编码、约束动作掩码及对技能、疲劳和地理上下文的注意力表示。NurseSchedRL 使用近端策略优化（PPO）配合可行性掩码，确保分配符合现实约束，并能动态适应患者到达和护士可用性的变化。在真实护士与患者数据的模拟中，该方法相比基线启发式和无约束强化学习方法，显著提升了调度效率，更好匹配了护士技能与患者需求，同时降低了疲劳水平。结果表明，强化学习在复杂高风险的医疗人力资源管理中具有重要应用潜力。
原文摘要 · Abstract (English)
Healthcare systems face increasing pressure to allocate limited nursing resources efficiently while accounting for skill heterogeneity, patient acuity, staff fatigue, and continuity of care. Traditional optimization and heuristic scheduling methods struggle to capture these dynamic, multi-constraint environments. I propose NurseSchedRL, a reinforcement learning framework for nurse-patient assignment that integrates structured state encoding, constrained action masking, and attention-based representations of skills, fatigue, and geographical context. NurseSchedRL uses Proximal Policy Optimization (PPO) with feasibility masks to ensure assignments respect real-world constraints, while dynamically adapting to patient arrivals and varying nurse availability. In simulation with realistic nurse and patient data, NurseSchedRL achieves improved scheduling efficiency, better alignment of skills to patient needs, and reduced fatigue compared to baseline heuristic and unconstrained RL approaches. These results highlight the potential of reinforcement learning for decision support in complex, high-stakes healthcare workforce management.
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。