基于韩国急诊室的对话,用于严重性选的人工智能
Jae Won Seo1, Sung-Joon Park2, Young Jae Kim3
1Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, Korea.
Scientific reports
|May 15, 2025
概括
人工智能驱动的自然语言处理 (NLP) 有效地从床边对话对紧急患者的严重程度进行分类. 这种方法有助于优先考虑护理,减少等待时间,并缓解急诊室过度拥挤.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理应用程序
- 紧急医疗 信息学 信息学
背景情况:
- 在急诊室有效的患者优先安排对于及时治疗至关重要.
- 目前的方法可能无法从动态对话中完全捕捉患者的敏度.
- 紧急部门的过度拥挤需要创新的解决方案.
研究的目的:
- 开发和评估人工智能驱动的自然语言处理 (NLP) 模型,用于自动的患者严重程度分类.
- 分析床边对话的成绩单,以实时评估敏度.
- 探索人工智能在改善急诊室工作流程和患者治疗结果方面的潜力.
主要方法:
- 利用了1028个急诊室床边对话记录的数据集.
- 应用常规机器学习 (SVM与TF-IDF) 和神经网络 (MLP) 模型.
- 采用十倍交叉验证,以确保使用AUROC的模型稳定性和性能评估.
主要成果:
- 支持矢量机 (SVM) 实现了0.764.76的接收器操作特征曲线 (AUROC) 下的最高面积.
- 多层感知器 (MLP) 显示出强大的性能,AUROC为0.759.
- 模型成功处理了对话,包括那些含有无意义或混乱内容的对话.
结论:
- 对患者对话的基于AI的NLP分析为自动严重程度分类提供了一种可行的方法.
- 这项技术可以大大减少急诊室等待时间和拥挤情况.
- 未来的研究可以利用像大型语言模型这样的先进模型进行进一步的改进.
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