预测入院与非计划回访的相关性:来自紧急护理机器学习模型的见解
Wayne A Martini1, Rachelle Beste2, Andrej Urumov1
1Department of Emergency Medicine, Mayo Clinic, Phoenix, Arizona.
The Journal of emergency medicine
|October 24, 2025
概括
机器学习 (ML) 模型预测急诊室 (ED) 的住院情况. 预测更高的入院分数与非预定回访 (URV) 的增加相关,这表明ML可以优化ED操作.
科学领域:
- 紧急医疗 紧急医疗
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 紧急部门 (ED) 越来越多地使用机器学习 (ML) 来预测住院和管理患者流动.
- 在ML驱动的入院预测和非计划回访 (URVs) 之间的关系需要进一步调查.
研究的目的:
- 评估实时ML预测入院概率和导致入院的72小时URV之间的关联.
- 为了分析这三个三级护理中心之间的关系.
主要方法:
- 对2023年1月1日至12月31日期间169,288次ED访问的分析.
- 利用内部开发的ML模型,结合了从电子健康记录中提取的47个临床特征.
- 从分析中排除了预期的回访.
主要成果:
- 在ML模型中,医院入院预测的AUC为0.88.
- 在72小时内共发生了1,996次URV (1.18%),其中6.61%涉及多次返回.
- 更高的预测入院得分与更高的URV率和随后的入院相关,最高得分组 (AI得分>75%) 的URV率为4.86%,返回后入院率为76.54%.
结论:
- 预测高的入院得分与更高的URV和入院病例发生率有关.
- ML模型显示了通过识别高风险患者以及时干预和改善资源配置来增强ED操作的潜力.
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