使用大型语言模型从自由文本电子健康记录中提取可扩展的信息
Bowen Gu1,2,3, Vivian Shao1, Ziqian Liao3
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030-R, Boston, MA, 02120, USA.
BMC medical research methodology
|January 28, 2025
概括
开源大型语言模型 (LLM) 可以准确地从电子健康记录 (EHR) 中提取健康的社会决定因素 (SDoH). 这些模型的性能优于传统方法,为临床研究提供了可扩展的解决方案,并改善了医疗保健结果.
科学领域:
- 计算语言学计算语言学
- 医疗信息学 医疗信息学
- 临床研究是临床研究.
背景情况:
- 电子健康记录 (EHR) 包含有关患者病史的有价值的自由文本数据.
- 从非结构化的电子健康记录中提取健康的社会决定因素 (SDoH) 是一个挑战.
- SDoH数据的有限实用性阻碍了研究和医疗保健改进.
研究的目的:
- 评估开源大型语言模型 (LLM) 在从临床笔记中提取SDoH时的准确性.
- 为了比较LLM的表现与传统的模式匹配方法.
- 评估在没有微调的情况下使用LLM用于SDoH数据提取的可行性.
主要方法:
- 使用Mass General Brigham (MGB) EHR数据进行横截面研究.
- 在200名患者的随机样本中手动标记SDoH方面.
- 通过使用准确度指标和宏观F1分数,对比基线模式匹配模型对八个开源LLM的评估.
主要成果:
- 实际上,LLM显著超过了基线模式匹配模型,特别是对于明确提到的SDoH.
- openchat_3.5模型在所有九个SDoH方面都显示出卓越的整体准确性.
- 在LLM管道中的快速工程减少了幻觉,提高了数据提取的准确性.
结论:
- 开源的LLM提供了一种有效和可扩展的方法,可以从非结构化的EHR数据中提取SDoH.
- 在SDoH数据提取中,LLM超越了传统的模式匹配技术.
- 进一步的发展,包括特定领域的培训,可以提高在临床研究和预测分析中LLM的实用性,以改善医疗保健结果和减少差异.
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