评估大型语言模型在生成临床相关的重症监护病房出院摘要方面的性能.
Seshadri C Mudumbai1,2, Philip Chung2, Ji-Qing Chen1
1From the Anesthesiology, Perioperative and Pain Medicine Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California.
A&A practice
|September 15, 2025
概括
大型语言模型 (LLM) 可以创建可读的ICU出院摘要,但它们往往错过了患者特定的细节,并且与人类专家相比缺乏深度. 为了临床准确性和实用性,需要进一步细化.
科学领域:
- 人工智能在医学中的应用
- 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:
- 大型语言模型 (LLM) 显示出用于自动化临床文档的前景,包括耗时的ICU出院摘要.
- 评估LLM生成的摘要的临床适用性对于采用至关重要.
研究的目的:
- 为了比较LLM生成的ICU出院总结与人类撰写的质量.
- 评估六个关键领域的LLM总结表现:连贯性,一致性,流性,相关性,实用性和整体质量.
主要方法:
- 从MIMIC-III数据库中使用了10例患者病例,每个病例都有20个医生笔记.
- 一个双向和自动回归变压器 (BART) 模型生成了总结.
- 四名密集医生使用5分利克特级别对LLM摘要进行了评估.
主要成果:
- 法学士总结在连贯性 (中位数4/5) 和流利性 (中位数4/5) 上得分很高,但在一致性 (中位数3/5),相关性 (中位数3/5),实用性 (中位数3/5) 上得分较低.
- 与人类总结相比,总体质量被评为低 (中位数为2/5).
- 临床医学常常忽略了患者特定的信息,影响了实用性和相关性.
结论:
- LLM可以生成流利和可读的ICU放电摘要.
- 目前的LLM需要进一步进行ICU特定的微调和领域知识集成,以提高临床细节的准确性和深度.
- 对于LLM来说,需要进行改进,以完全符合专家临床医生标准.
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