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English(EN) Do Machine Learning-Guided Proactive Full Capacity Protocols Mitigate Emergency Department Overcrowding? A Simulation-Based Evaluation

机器学习预测并预防医院急诊科拥挤

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究探讨了使用机器学习主动管理急诊科（ED）拥挤问题。研究人员开发了一个仿真框架，该框架使用机器学习提前六小时预测拥挤情况，并触发主动的满负荷协议（FCPs）。该仿真基于一家大型美国医院的数据，结果显示与传统的被动方法相比，主动激活FCPs显著减少了急诊科超出拥挤阈值的时间，并缩短了协议持续时间。 AI

影响 机器学习指导的主动容量管理可以通过预防急诊科拥挤来提高医院效率和患者护理水平。

排序理由 该集群包含一篇详细介绍机器学习应用基于仿真的评估的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

机器学习预测并预防医院急诊科拥挤

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该集群包含一篇详细介绍机器学习应用基于仿真的评估的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Khalid Aram, Orhun Vural, Bunyamin Ozaydin, James Booth, Abdulaziz Ahmed ·

    Do Machine Learning-Guided Proactive Full Capacity Protocols Mitigate Emergency Department Overcrowding? A Simulation-Based Evaluation

    arXiv:2610.30148v1 Announce Type: cross Abstract: Objective: Emergency department (ED) overcrowding threatens care quality, yet Full Capacity Protocols (FCPs) are typically activated only after crowding thresholds have been exceeded. This study evaluated whether machine learning-…

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