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日本放射学报告的零射击信息提取和聚类的大型语言模型方法:算法开发和验证

Yosuke Yamagishi¹, Yuta Nakamura², Shouhei Hanaoka¹

¹Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

|January 26, 2025

概括

这项研究开发了一种大型语言模型 (LLM) 算法,用于从日本肺癌放射学报告中提取信息. 与以前的方法相比,基于LLM的方法在集群报告中表现优越.

科学领域:

医疗信息学医学信息学
自然语言处理自然语言处理.
放射学中的人工智能

背景情况:

自然语言处理 (NLP) 对于构建医疗数据,特别是放射学报告至关重要.
聚类技术通过识别类似的疾病模式来提高数据的实用性.
非英语医学数据集的有限可用性阻碍了对不同语言的NLP模型开发.

研究的目的:

开发和评估一个大型语言模型 (LLM) 算法,用于提取和聚类日本肺癌放射学报告的信息.
为了比较基于LLM的方法与以前的监督方法的性能.

主要方法:

使用了 MedTxt-RR 数据集,其中包括 135 份日本放射学报告.
雇佣了一名法学士来提取关键的肺癌发现并将其转换为数值特征.
应用K- 媒介聚类用于分析.
评估信息提取准确性和集群性能,使用标准化相互信息和Fowlkes-Mallows指数等指标.

主要成果:

瘤大小提取精度为73.3%,位置和横向性为83%.
对测试数据的集群性能实现了0.6414的正常化相互信息,0.5598的调整相互信息,以及0.5354.5的Fowlkes-Mallows指数.
拟议的LLM方法在所有评估指标上表现优于以前的监督方法.

结论:

无监督的LLM方法在聚合日本放射学报告方面表现出色,超过监督的方法.
在医学报告中提取信息,并将其整合到知识结构中,LLM具有显著的潜力.

关键词:

聚类集群是指聚类的聚类.提取信息提取信息大型语言模型肺癌是一种肺癌.机器学习是机器学习.自然语言处理自然语言处理.放射学报告放射学报告

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