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一个机器学习策略来预测高敏度儿童的数量,他们离开急诊室没有被看到

Brandon Kappy1,2, Patrick Butler3, Yiqi Su3

  • 1Division of Emergency Medicine, Children's National Hospital, Washington, District of Columbia, USA.

Journal of the American College of Emergency Physicians open
|January 16, 2026
PubMed
概括

机器学习模型准确地预测了在急诊室 (ED) 留下没有被看的高急性儿科患者 (LWBS). 这可以帮助针对性干预减少LWBS事件.

关键词:
人工智能的人工智能是人工智能.拥挤的人群拥挤紧急医疗 紧急医疗机器学习是机器学习.运营研究研究 运营研究儿科急诊医学 儿科急诊医学

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