重新定义医疗保健数据互操作性:在信息交换中实证探索大型语言模型
Dukyong Yoon1,2,3, Changho Han1, Dong Won Kim1
1Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
大型语言模型 (LLM) 可以通过准确地转换和传输医疗信息来改善医疗数据交换. 这项技术增强了互操作性,而不需要复杂的术语或数据结构标准化.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
背景情况:
- 医疗数据交换和互操作性受到非标准化和非结构化的医疗记录的阻碍.
- 大型语言模型 (LLM) 为这些信息交换挑战提供了潜在的解决方案.
研究的目的:
- 评估LLM在转换和传输医疗保健数据方面的能力.
- 评估LLM在支持医疗数据互操作性方面的表现.
主要方法:
- 使用来自MIMIC-III和英国生物库的数据进行了三项实验.
- 实验1:评估LLM在将结构化实验室结果转换为非结构化格式的准确性.
- 实验2:比较基于LLM的诊断代码转换 (ICD-9-CM到SNOMED-CT) 与传统的映射. 实验3:专注于从非结构化的临床笔记中提取信息.
主要成果:
- 基于文本的LLM方法在实验室结果转换方面表现出高准确性,并改善了诊断代码转换的一致性.
- 该LLM在从非结构化记录中提取仿制药名称时实现了87.2%的积极预测值.
结论:
- 通过准确和高效的数据转换和交换,LLM显示了提高医疗保健数据互操作性的巨大潜力.
- 在不需要复杂的术语和数据结构标准化的情况下,LLM可以改善医疗数据交换.
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