机器学习在急救部门的实施:系统性审查
Banafshe Hosseini1,2,3, Atushi Patel1, Megan Landes4,5
1Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Toronto, Canada.
Digital health
|February 2, 2026
概括
机器学习模型在急诊室显示出有前途的预测患者的结果和优化操作. 然而,为了更广泛的临床采用,必须解决通用性和工作流集成方面的挑战.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 机器学习 (ML) 越来越多地被用于紧急部门 (ED) 的应用.
- 评估实施的ML模型的临床和操作影响对于理解它们在现实世界中的实用性至关重要.
研究的目的:
- 在ED环境中对机器学习模型实施的研究进行系统审查.
- 评估这些ML模型的临床和操作影响.
主要方法:
- 在多个数据库中进行了全面的文献搜索,截至2024年1月.
- 包括专注于在ED中实施和评估ML模型的研究.
主要成果:
- 审查了84项研究,其中渐变增强和神经网络是常见的.
- ML模型在预测死亡率 (AUC 0.618-0.978),处置 (AUC 0.675-0.96) 和停留时间方面表现出有效性.
- 等待时间预测模型将等待时间减少了18% - 26%,而败血症检测显示出有前途.
结论:
- 在ED研究中,ML很普遍,但一般化和工作流集成阻碍了临床使用.
- 提高数据质量,表示和验证是提高EDsML在现实世界的实用性的关键.
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