使用机器学习技术来管理紧急选流
Mohammed Almulhim1, Dunya Alfaraj1, Dina Alabbad2
1Emergency Medicine Department, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia.
概括
与传统的护理评估相比,用于急诊室分拣的新机器学习模型显著降低了错误分拣率. 这种人工智能工具提高了患者分类的准确性和紧急护理的效率.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- triage 在急诊室是必不可少的,但当前的系统往往导致错误的患者分类.
- 人工智能 (AI) 和机器学习 (ML) 为改善患者分类和分类准确性提供了潜在的解决方案.
研究的目的:
- 开发和评估一种机器学习模型,用于预测急诊室患者分拣水平.
- 为了比较ML模型与标准护理分类系统的性能.
主要方法:
- 一项回顾性试点研究利用了大学国王法哈德医院 (2020年1月至2022年12月) 的急诊室记录.
- 使用了998名随机选择的患者的数据集,并通过10倍交叉验证训练了ML模型.
- 采用了两种实验设置:一种是五个分拣级别,另一种是2-5级别的组合.
主要成果:
- 在第一个实验中,ML模型的准确性达到84%,在第二个实验中达到64%.
- 最重要的是,机器学习模型的错误分拣率明显低于标准护理分拣系统.
结论:
- 拟议的机器学习模型提供了优越的准确性和较低的错误分选率与传统的护理分选相比.
- 这种人工智能驱动的方法可以成为优化急诊室患者管理的宝贵工具,通过更高效,更准确的分组.
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