在医疗对话中通过多级学习和快速识别症状
Hua Wang1, Xue-Feng Bai1, Xiu-Tao Cui2,3
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.
Journal of medical systems
|August 20, 2025
概括
这项研究引入了一种利用多个实例学习和即时指导注意力从医疗对话中提取症状的新方法. 该方法通过在对话中识别特定的症状来提高电子健康记录 (EHR) 的准确性.
科学领域:
- 自然语言处理
- 医疗信息学
- 机器学习
背景情况:
- 电子健康记录 (EHR) 系统需要从医疗对话中自动提取症状.
- 挑战包括分散的线索,非正式的语言,以及患者描述中的模两可/否定陈述.
- 现有的模式难以进行非结构化的多轮对话.
研究的目的:
- 在医疗对话中开发一种新的细粒度症状识别方法.
- 提高自动化医疗记录的准确性和效率.
- 解决分散信息,非标准术语和患者报告症状的否定的挑战.
主要方法:
- 一个多实例学习 (MIL) 框架将对话视为提升记忆的发言袋.
- 快速引导的注意力利用标准化的术语来识别同义词,隐含的提及和否定,提高准确性.
- 使用R-Drop规范化来提高对噪音数据的稳定性.
主要成果:
- 拟议的方法获得了85.93%的F1得分,比基准水平高出约8%.
- 实现了85.09%的精度和86.83%的回忆,显示出强大的性能.
- 成功识别了特定的症状表达对, 展示了细粒度的提取能力.
结论:
- 结合MIL和即时指导的关注方法有效地解决了从医疗对话中提取症状的挑战.
- 这种方法可以准确记录症状,提升自动化医疗信息提取和智能电子健康记录系统.
- 细粒度提取支持诊断决策,并提高了医疗记录的质量.
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