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Updated: Sep 14, 2025

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预测急诊室患者数量使用极端梯度提升与时间和季节性特征工程:跨医院的比较研究

Kian A Huang1, William M Hardin1, Neelesh S Prakash1

  • 1Radiology, University of South Florida Morsani College of Medicine, Tampa, USA.

Cureus
|July 21, 2025
PubMed
概括

一个极端梯度提升 (XGBoost) 模型准确地预测了急诊室 (ED) 患者数量. 这种机器学习方法显著改善了医院资源规划的传统方法.

关键词:
人工智能和机器学习ed 过度拥挤 过度拥挤紧急医疗研究 紧急医疗研究预测性的医疗分析.预测建模预测建模

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