在急诊室的COVID-19分拣2.0:分析和AI如何改变人类制造的算法来预测临床途径
Christina C Bartenschlager1,2,3, Milena Grieger1, Johanna Erber4
1Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany.
Health care management science
|July 10, 2023
概括
一个针对COVID-19患者途径的人造分拣算法显示精度低. 人工智能和互动的人工智能方法显著改善了分拣性能,提供了更好的患者护理和医院容量规划.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 紧急医疗 紧急医疗
背景情况:
- 随着Covid-19大流行,医院资源紧张,需要有效的患者分拣.
- 伦理考虑和临床途径的确定对于患者护理和医院容量规划至关重要.
研究的目的:
- 为了评估人类为Covid-19患者途径的分拣算法的性能.
- 探索AI,可解释AI (XAI) 和交互式技术的潜力,以提高分拣准确度和灵敏度.
主要方法:
- 来自LEOSS注册表的4000多名欧洲COVID-19患者的多中心数据集的分析.
- 对比人类制造的算法与人工智能驱动的和交互式的人类-人工智能算法.
- 评估性能指标,包括准确性和敏感性,独立于数据准备方法.
主要成果:
- 人造的分类算法在病房分类中只达到28%的准确率和15%的灵敏度.
- 人工智能和分析显著提高了分拣性能.
- 一个交互式的人类-人工智能算法以73%的准确度和76%的灵敏度展示了卓越的结果.
- 添加一种息护理类别并没有改善绩效.
结论:
- 对于Covid-19患者途径的现有的人造分拣算法需要显著改进.
- 人工智能和互动的人工智能系统对优化急诊室分拣和医院容量管理有很大的前景.
- 未来的研究应该专注于开发和验证先进的AI驱动的分拣系统.
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