机器学习模型用于预测儿科急诊室的入院情况:系统性审查
Guillem Brullas1, Carles Luaces2, Victoria Trenchs2
1Environment Effects on Child/Adolescent Well-being Research Group, Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat (Barcelona), Spain; Pediatric Emergency Department, Hospital Sant Joan de Déu (HSJD), Esplugues de Llobregat (Barcelona), Spain; Doctoral Program in Medicine and Translational Research, School of Medicine and Health Sciences, Universitat de Barcelona (UB), Barcelona, Spain.
International journal of medical informatics
|November 20, 2025
概括
机器学习模型对预测儿科住院患者的预测有前途,但目前的研究往往缺乏严格的方法和透明的报告. 未来的研究需要前性验证和更清晰的报告,以便可靠的临床使用.
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:
- 儿科急诊室 (PEDs) 由于医院入院决定延迟而过度拥挤.
- 机器学习 (ML) 模型为PEDs的住院预测提供了早期预测潜力.
研究的目的:
- 系统地审查和评估用于预测PED住院治疗的ML模型.
- 评估这些模型的开发,验证,质量,偏差风险和适用性.
主要方法:
- 搜索了PubMed,Cochrane,科学网,Scopus直到2025年2月25日.
- 包括开发/验证ML模型用于PED住院预测的研究.
- 不包括案例报告,综述,元分析,非英语/西班牙语研究.
- 使用PROBAST + AI工具评估质量,偏差和适用性.
主要成果:
- 包括19项研究;大多数缺乏前性或外部验证.
- 常见的预测因素包括年龄,性别,首席投诉,到达模式和分拣类别.
- 模型的性能差异很大 (AUC-ROC 0.624-0.968),经常存在方法不一致和报告不良.
- 只有一个研究得到了良好的质量评估.
结论:
- 机器学习模型显示了在PED中支持早期住院决策的潜力.
- 目前的研究显示出重要的方法和报告限制.
- 未来的研究必须侧重于严格的设计,未来的外部验证和透明的报告.
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