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变压器和大型语言模型是电子健康记录研究的高效特征提取器

Kevin Yuan¹, Chang Ho Yoon², Qingze Gu³

¹Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK. kevin.yuan@ndph.ox.ac.uk.

Communications medicine

|March 22, 2025

概括

现代自然语言处理 (NLP) 和大型语言模型 (LLM) 可以准确地从电子健康记录中提取感染类型. 这种方法比传统的编码方法更频繁地揭示特定的感染源.

科学领域:

医疗信息学医疗信息学
计算语言学计算语言学
临床研究临床研究

背景情况:

电子健康记录包含大量的自由文本数据.
信息提取方面的挑战往往导致使用不那么具体的临床代码.

研究的目的:

评估NLP和LLM在从自由文本临床笔记中提取感染类型方面的有效性.
将先进模型的性能与传统方法和现有的编码系统进行比较.

主要方法:

利用了来自英国牛津郡的938,150份医院抗生素处方数据集.
训练了包括Bio+Clinical BERT,GPT-3.5和GPT-4在内的各种模型,以从自由文本指示中推断感染类型.
在内部和外部测试数据集上使用F1分数进行模型性能比较.

主要成果:

一个微调的生物+临床BERT模型获得了最高的性能 (F1平均得分为0.97-0.98).
零射击GPT-4与没有标记数据的传统NLP模型相匹配 (F1 0.71-0.86).
自由文本的指示比ICD-10代码更频繁地确定了31%的特定感染源.

结论:

基于变压器的模型显示出在医学中广泛使用的巨大潜力.
这些模型可以增强从结构化的自由文本记录中提取信息.
改进的数据提取可以促进更好的临床研究和患者护理.

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