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通过生成模型进行生物医学文本规范化

Jacob S Berkowitz¹, Apoorva Srinivasan¹, Jose Miguel Acitores Cortina¹

¹Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N San Vicente Blvd, Pacific Design Center Suite G540, West Hollywood, CA 90069 United States.

Journal of biomedical informatics

|May 17, 2025

概括

检索增强生成规范化 (RAGnorm) 有效地标准化了非结构化的电子健康记录 (EHR) 文本. 这种基于LLM的方法在临床文本规范化任务中表现出卓越的表现.

科学领域:

生物医学信息学生物医学信息学
自然语言处理自然语言处理.
人工智能的人工智能

背景情况:

电子健康记录 (EHR) 包含大量的非结构化文本数据.
电子健康记录文本的格式不一致阻碍了其在预测建模和临床决策支持中的使用.
大型语言模型 (LLM) 提供了医疗文本标准化的潜力,因为它们的上下文理解.

研究的目的:

使用LLMs开发和评估临床文本规范化管道.
评估各种基于LLM的规范化策略的有效性.
将LLM的表现与医学文本标准化的传统方法进行比较.

主要方法:

实施了四种基于LLM的规范化策略:零射击回忆,即时回忆,语义搜索和检索增强代规范化 (RAGnorm).
包括使用基于TF-IDF的字符串匹配的基线.
评估了三个SNOMED映射条件术语数据集 (瘤学,机构样本,常用代码) 和TAC 2017药物标签 (MedDRA术语) 的性能.
使用平均最短路径长度和微F1得分来测量性能.

主要成果:

在所有评估的数据集中,RAGnorm表现出卓越的性能.
在瘤学数据集上,RAGnorm的平均最短路径长度为0.21.
在TAC 2017任务4中获得了88.01的微F1得分,优于其他模型.

结论:

以检索为重点的LLM方法,如RAGnorm,克服了传统LLM在文本规范化方面的局限性.
RAGnorm 和类似的检索技术显示出对生物医学自由文本的规范化有重大前景.
建议对这些方法进行进一步的探索,以促进临床数据的利用.

关键词:

临床文本规范化标准化大型语言模型.快递工程是指快递的工程.提取增强生成的提取.

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