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预测住院患者从急诊室 triage 使用机器学习:一个系统审查

Ethan L Williams^1,2, Daniel Huynh³, Mohamed Estai⁴

¹School of Medicine, The University of Notre Dame, Fremantle, Western Australia, Australia.

Mayo Clinic proceedings. Digital health

|April 10, 2025

概括

机器学习模型显示出从急诊室数据预测医院入院的准确性. 需要进一步的研究来解决偏见,并提高患者流量管理的现实应用性.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
临床决策支持系统临床决策支持系统

背景情况:

紧急服务部门面临的挑战是患者流量管理.
准确预测住院患者的入院情况对于优化资源配置至关重要.
机器学习 (ML) 提供了提高预测准确性的潜力.

研究的目的:

评估ML模型的证据质量,从急诊室 (ED) 选数据预测住院患者的入院情况.
评估现有的ML模型的方法严谨性和性能.
确定差距和未来的研究方向,以加强患者流量管理.

主要方法:

根据PRISMA指南 (2014-2024) 进行全面的文献搜索 (PubMed,Embase,科学网,Scopus,CINAHL).
包括31个符合特定标准的英语语言研究.
使用PROBAST和修改后的TRIPOD+AI框架评估模型质量.
分析报告的模型性能指标,包括接收器操作特征下的面积 (AUROC).

主要成果:

七项研究显示了严格的方法和强大的in silico性能 (AUROC 0.81-0.93).
其余的24项研究由于异质性,偏差风险不清楚到高以及适用性问题而存在局限性.
目前的文献表明,仅使用分拣数据预测入院的真准确度很好.

结论:

机器学习模型展示了从ED分拣数据准确预测住院患者入院的潜力.
显著的异质性和偏见风险需要谨慎地解释当前证据.
未来的研究应侧重于透明的开发,时间验证和现实世界的影响分析,以改善患者流动.

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