临床文本总结:适应大型语言模型可以超过人类专家的表现.
Dave Van Veen1,2, Cara Van Uden2,3, Louis Blankemeier1,2
1Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Research square
|November 14, 2023
概括
大型语言模型 (LLM) 现在在跨多个任务总结临床文本方面表现优于人类专家. 自然语言处理 (NLP) 的这一进步可以减少临床医生的文档负担,改善患者的护理.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 临床医生面临着大量的时间负担,总结电子健康记录 (EHR).
- 大型语言模型 (LLM) 对NLP有希望,但需要在各种临床总结任务中进行验证.
研究的目的:
- 严格评估适应领域的LLM在各种临床文本总结任务上的有效性.
- 用医生读者研究来比较LLM生成的摘要与人类生成的摘要.
主要方法:
- 在六个数据集和四个临床总结任务 (放射学报告,患者问题,进展说明,医生与患者对话) 中应用领域适应八个LLM.
- 对模型性能和适应方法进行了定量评估.
- 与十名医生进行了临床读者研究,以评估摘要质量 (完整性,正确性).
- 与读者研究成绩相关联的传统NLP指标.
主要成果:
- 确定了不同LLM和适应策略之间的权衡.
- 证明最适应的LLM产生了医生比人类摘要更喜欢的摘要.
- 发现最近的LLM进步并不总是保证更好的表现.
结论:
- 这项研究提供了第一个证据,证明LLM在多任务临床文本总结方面表现优于人类专家.
- 将适应的LLM整合到临床工作流程中可能会减轻文档负担.
- 通过LLM学位,临床医生可以将更多的时间用于患者护理和医学的人性方面.
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