数字写字员的开发和评估:为紧急部门提供对话总结管道,为减少文档负担提供咨询会议
Emre Sezgin1,2, Joseph Sirrianni1, Kelly Kranz1
1Nationwide Children's Hospital, Columbus OH.
medRxiv : the preprint server for health sciences
|December 18, 2023
概括
这项研究引入了一个AI数字写字器来总结急诊室的临床对话. 精心调整的BART-Large-CNN模型显示出有前途的性能,突出了AI.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 临床文档 临床文档
背景情况:
- 临床文档对医疗保健专业人员来说是一个很大的负担.
- 有效的临床对话总结对于有效的患者护理和记录保存至关重要.
- 现有的临床笔记生成方法通常需要手动输入,并且耗时.
研究的目的:
- 开发和评估一个概念验证的数字写字系统,用于总结急诊部 (ED) 的临床对话.
- 为了比较四个预训练的大型语言模型 (LLM) 在临床对话总结中的性能.
- 评估基于LLM的总结中零射击和微调方法的有效性.
主要方法:
- 用四个预先训练好的大型语言模型 (T5-small,T5-base,PEGASUS-PubMed,BART-Large-CNN) 来构建数字写字系统.
- 使用了来自ED临床医生的100次转诊对话和相关医疗记录的数据集.
- 模型性能使用ROUGE分数 (ROUGE-1,ROUGE-2,ROUGE-L) 和手动注释生成的总结进行质量评估.
主要成果:
- 微调的BART-Large-CNN模型获得了最高的性能,ROUGE F1得分为ROUGE-1的0.49,ROUGE-2的0.23和ROUGE-L的0.35.
- 在PEGASUS-PubMed模型中,表现较差 (ROUGE-1:0.28,ROUGE-2:0.11,ROUGE-L:0.22).
- 与微调相比,在使用零射击方法时,BART-Large-CNN的性能下降了50%以上. 手动注释显示回忆率为71.4%,关键信息识别准确率为67.7%.
结论:
- 微调的BART-Large-CNN模型显示了临床对话总结的巨大潜力,证明了对临床对话结构的强烈掌握.
- 虽然回忆率很高,但准确度的变化表明模型改进的领域.
- 像这个数字写字器这样的人工智能辅助工具可能会减轻临床文档工作负担,保证使用更大的模型和比较分析进行进一步的研究.
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