English(EN) Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study

斯坦福大学研究人员将RL用于儿科超声心动图实验室资源分配

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

斯坦福大学和Lucile Packard儿童医院的研究人员开发了一个模拟模型，以优化儿科超声心动图实验室的资源分配。他们将即时患者分配与基于预订的系统进行了比较，发现即时分配显著缩短了等待时间。训练了一个Dueling Double Deep Q-Network来近似最优的动态即时策略，其性能与最佳的基于规则的即时策略相当。 AI

影响这项研究展示了强化学习在优化医疗保健运营效率方面的应用。

排序理由该集群包含一篇学术论文，详细介绍了使用强化学习进行资源分配的模拟研究。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein, Frederick Damen, Alison L. Marsden, Daniele E. Schiavazzi · 2026-10-15 04:00

儿科超声心动图实验室资源分配的基于规则和强化学习的策略：一项模拟研究

arXiv:2506.06297v2 Announce Type: replace Abstract: Scheduling echocardiographic examinations in a hospital setting presents significant challenges due to non-deterministic factors such as patient no-shows, variable arrival times, and heterogeneous exam durations, as well as asym…

本文如何被排名

报道来源 [1]

儿科超声心动图实验室资源分配的基于规则和强化学习的策略：一项模拟研究

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