使用大型语言模型从紧急部门的笔记中提取核心损伤信息
Dong Hyun Choi1,2, Yoonjic Kim2,3,4, Sae Won Choi5
1Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.
Journal of Korean medical science
|December 3, 2024
概括
大型语言模型 (LLM) 可以有效地从急救部门 (ED) 的笔记中提取伤害数据,改善监视. 这项研究表明,LLM为伤害信息提取任务提供了多功能解决方案.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生监督 公共卫生监督
背景情况:
- 伤害是全球主要的健康负担,发病率和死亡率很高.
- 有效的伤害监测对于预防至关重要,但资源密集.
- 需要开发自动化方法来从临床笔记中提取伤害数据.
研究的目的:
- 开发和验证可在本地部署的大型语言模型 (LLM) 来从急诊部 (ED) 的临床笔记中提取核心损伤信息.
- 评估一个可概括的Llama-2模型和双向编码器从变压器 (BERT) 模型的表现在伤害数据提取中的性能.
- 将LLM的准确性和效率与传统伤害监测方法进行比较.
主要方法:
- 一项诊断研究利用了来自两个城市学术三级医院的2014年1月至2020年12月的回顾性数据.
- 一个可通用的Llama-2模型和五个BERT模型被微调为从ED笔记中提取信息 (机制,地点,活动,意图,严重程度) 的任务.
- 模型性能使用准确度和宏观平均F1分数进行评估,伤害登记数据作为黄金标准.
主要成果:
- 拉玛-2模型在各种任务中表现出高准确度,包括伤害机制 (0.899),意图 (0.972) 和严重程度 (0.935).
- 在导出和测试队列中,Llama-2在所有提取任务中在准确性和宏观平均F1分数方面始终优于BERT模型.
- 限制Llama-2模型以避免不确定的预测进一步提高了它的准确性.
结论:
- 可在本地部署的LLM在从非结构化的ED临床笔记中提取关键受伤相关信息方面表现强.
- 生成型LLM为各种伤害信息提取需求提供了多功能和高效的解决方案.
- 这些发现支持将LLM整合到公共卫生监测系统中,以改善伤害预防工作.
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