大型语言模型使用现实世界的电子医疗记录数据准备出院摘要显示出有希望的结果
Lewis Hains1, Oliver Kleinig1, Ashwin Murugappa1
1Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
大型语言模型 (LLM) 在从临床笔记中生成出院摘要方面表现有前途. 两个经过测试的LLM的表现类似,表明可能减少临床医生的行政负担.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 准备出院摘要是临床医生耗时的行政任务.
- 大型语言模型 (LLM) 是新兴的工具,在医疗保健中具有潜在的应用.
研究的目的:
- 评估两种不同的LLM在生成排放摘要方面的有效性.
- 用一个验证的评分指标来评估LLM生成的放电摘要的性能.
主要方法:
- 使用两个LLM (llama3:instruct和lama3:70b) 来从实际临床文档中生成出院摘要.
- 使用经过验证的排放摘要评分指标来评估生成的摘要的质量.
主要成果:
- 这两种LLM在生成放电摘要方面表现相似.
- llama3:instruct 模型的平均得分为 19.1/31 (SD: 2.42).
- 拉玛3:70b模型的平均得分为19.2/31 (SD:3.48).
结论:
- 在生成排放摘要方面,LLM表现出相似的有效性.
- 在排放总结生成中使用LLM可能会减轻临床行政工作负担.
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