数字书记的评价:对话总结 紧急部门咨询电话 咨询电话
Emre Sezgin1, Joseph Winstead Sirrianni2, Kelly Kranz1
1Nationwide Children's Hospital, Columbus, United States.
Applied clinical informatics
|May 15, 2024
概括
使用人工智能的数字写字系统显示,它可以总结急诊部 (ED) 的临床对话. 微调的BART-Large-CNN模型在这个临床文档支持工具中实现了最佳性能.
科学领域:
- 医疗保健中的人工智能
- 用于临床文档的自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 临床文档是医疗保健提供者的耗时但必不可少的任务.
- 紧急部门 (ED) 的咨询产生了复杂的对话数据,需要有效的总结.
- 大型语言模型 (LLM) 为自动化临床笔记生成提供了潜力.
研究的目的:
- 开发和评估一个概念验证的数字写字系统,用于总结ED咨询电话.
- 评估各种预训练的LLM在临床对话总结中的表现.
- 通过自动总结来支持临床文档工作流程.
主要方法:
- 使用了四个经过预训练的LLM (T5-小,T5-基,PEGASUS-PubMed,BART-大-CNN) 的数据.
- 模型在100个ED推对话的数据集上使用零射击和微调方法进行了评估.
- 使用ROUGE分数 (ROUGE-1,ROUGE-2,ROUGE-L) 和简要质量的手动注释来衡量性能.
主要成果:
- 精心调整的BART-Large-CNN模型获得了最高的ROUGE分数 (F1ROUGE-1=0.49,F1ROUGE-2=0.23,F1ROUGE-L=0.35). 在此过程中,该模型获得了最高的ROUGE分数 (F1ROUGE-1=0.49,F1ROUGE-2=0.23,F1ROUGE-L=0.35).
- 在PEGASUS-PubMed中,其性能显著降低 (F1ROUGE-1=0.28,F1ROUGE-2=0.11,F1ROUGE-L=0.22).
- 巴特-大型-CNN的零射击性能比其微调性能低50%以上,回忆率为71.4%,注释准确率为67.7%.
结论:
- 巴特-大型-CNN模型展示了对临床对话结构的强烈理解,特别是在微调后.
- 虽然召回率很高,但性能的一致性和准确性需要进一步改进.
- 人工智能辅助工具显示出提高临床文档效率的潜力.
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