在临床对话中识别演讲者的角色
Andrew Zolensky1, Kuk Jin Jang2, Janice Sabin3
1Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
medRxiv : the preprint server for health sciences
|November 24, 2025
概括
大型语言模型 (LLM) 现在可以在临床对话中自动识别说话者. 这项被称为演讲者角色识别 (SRI) 的技术,可以在没有手动审查的情况下改善对患者和临床医生的沟通的理解.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 患者与临床医生的沟通对于医疗保健结果至关重要.
- 手动分析临床相互作用是耗时且具有挑战性的.
- 现有的自动语音识别系统缺乏扬声器角色识别能力.
研究的目的:
- 调查大型语言模型 (LLM) 在临床环境中用于演讲者角色识别 (SRI) 的有效性.
- 为了比较LLM的表现与没有准确的日记化标识符.
- 评估识别器腐败对SRI性能的影响.
主要方法:
- 使用BERT,一个大型语言模型,用于扬声器角色识别 (SRI).
- 采用垂直转折细分和日记化标识符.
- 微调模型具有不同级别的识别器腐败,以评估性能.
主要成果:
- 仅使用语言信号,BERT的准确率达到82%,F1得分达到82%.
- 整合精确的日记识别器提高了性能,达到95%的准确性和95%的F1分数.
- 即使有损坏的日志化标识符,LLM的表现也很强.
结论:
- 精心调整的LLM在临床成绩单中显示出准确的演讲者角色识别 (SRI) 的巨大潜力.
- LLM提供了一种自动化解决方案,用于分析患者-临床医生沟通动态.
- 这种方法可以增强临床话语和医疗保健结果的研究.
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