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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.

medRxiv : the preprint server for health sciences

|March 17, 2025

概括

检索增强生成 (RAGnorm) 有效地将电子健康记录中的非结构化临床文本正常化. 这种方法超越了传统方法,改善了数据标准化,以获得更好的医疗保健应用.

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,这给数据分析和利用带来了挑战.
医学文本的格式不一致阻碍了预测建模,临床决策支持和数据挖掘.
大型语言模型 (LLM) 在理解和标准化复杂的医学语言方面表现有前途.

研究的目的:

开发和评估使用LLMs的临床文本规范化管道.
评估基于LLM的不同规范化策略与基线方法的有效性.
改善非结构化医疗文本的标准化,以提高数据的可用性.

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生成 (RAGnorm).
包括使用基于TF-IDF的字符串匹配的基线方法.
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药物注释 (MedDRA) 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.
RAGnorm实现了最低的平均最短路径长度:0.21 (特定于域),0.58 (采样) 和0.90 (顶部术语).
在TAC 2017任务4上,RAGnorm获得了88.01的微F1得分,超过了没有先前培训数据的其他模型.

结论:

专注于检索的LLM方法有效地解决了临床文本规范化的局限性.
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很大的潜力.
建议对这些方法进行进一步的探索,以推进生物医学自然语言处理.

关键词:

临床文本规范化标准化大型语言模型.快速的工程迅速的工程提取增强生成的提取

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