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, Andrew.Zolensky@PennMedicine.upenn.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
大型语言模型 (LLM) 现在可以自动识别临床对话中的说话角色. 这项技术通过准确区分医生,患者和其他护理人员来改善患者-临床医生沟通分析.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 医疗保健中的人工智能
背景情况:
- 患者与临床医生的沟通对于理解医疗保健相互作用和结果至关重要.
- 手动分析临床话语是耗时且具有挑战性的,特别是在演讲者角色识别 (SRI) 方面.
- 现有的自动语音识别系统与日记不具备将特定角色分配给演讲者的能力.
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.
- 用单独的语言特征来评估SRI性能,而不是使用日记化标识符.
- 评估识别器腐败对基于LLM的SRI准确性的影响.
主要方法:
- 利用BERT,一个大型语言模型,用于临床成绩单上的演讲者角色识别 (SRI).
- 采用垂直转折细分和日记化标识符.
- 微调了BERT模型,使用不同级别的标识符腐败来测试性能稳定性.
主要成果:
- 通过仅使用语言信号,BERT实现了82%的准确性和82%的SRIF1得分.
- 整合精确的日记识别器显著提高了性能,达到95%的准确性和95%的F1分数.
- 该研究表明,LLM在临床环境中具有SRI的能力.
结论:
- 精心调整的大型语言模型 (LLM) 在临床成绩单中对自动发言者角色识别 (SRI) 非常有效.
- 法律法规提供了一个强大的解决方案,用于分析患者和临床医生的沟通动态.
- 将LLM与准确的日记记录相结合,进一步提高了SRI在医疗保健环境中的精度.
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