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在放射学报告中增强实体分类和关系映射的两阶段大语言模型方法

Chaiho Shin¹, Dareen Eom¹, Sang Min Lee²

¹Interdisciplinary Program of Medical Informatics, College of Medicine, Seoul National University, Seoul, Republic of Korea.

Scientific reports

|August 27, 2025

概括

本研究介绍了使用变压器 (BERT) 的双向编码器表示和大语言模型 (LLM) 分析放射学报告的两阶段管道. 该方法通过结构化放射性发现提高了医学图像标签的诊断准确性.

科学领域:

医疗信息学
自然语言处理
放射学

背景情况:

放射学报告呈现语言的多样性, 具有挑战性的自动化分析和医学图像标签.
精确的放射性发现和时间性疾病模式对于临床决策至关重要.

研究的目的:

开发和评估一个两阶段的自然语言处理管道,集成来自变压器的双向编码器表示 (BERT) 和大型语言模型 (LLM).
改进放射学报告的分析,用于胸部CT的病变位置映射和脑MRI的诊断情节映射.

主要方法:

开发了两阶段的管道:使用BERT识别和分类实体的实体关键分类,然后使用LLM推断提取实体之间的关系关系.
通过首尔阿桑医疗中心的40多万份放射学报告进行了培训和验证.

主要成果:

该管道实现了胸部CT病变位置映射的宏F1分数为77.39.
该管道在脑MRI诊断 - 情节映射中获得了70.58的宏观F1得分.
整合BERT和LLM在提高放射学报告分析的诊断准确性方面表现出有效性.

结论:

拟议的BERT和LLM综合管道有效分析放射学报告,改善发现的结构和时间模式捕获.
这种方法在医学图像标签和放射学诊断准确性方面具有重大潜力.

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