术语候选人世代以大型语言模型丰富临床术语
Amila Kugic1, Stefan Schulz1, Markus Kreuzthaler1
1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究使用临床文档来寻找国际疾病分类第10版 (ICD-10) 代码的新术语. 利用大型语言模型,它增强了临床术语,弥合了术语和标准化代码之间的差距.
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.
- 临床术语学 临床术语学
背景情况:
- 临床常规文档为二次使用提供了有价值的数据.
- 确定像ICD-10这样的标准化代码的全面术语仍然是一个挑战.
研究的目的:
- 为了利用注释的临床数据来识别ICD-10代码的其他术语候选人.
- 为了弥合现实世界临床术语和标准化术语之间的词汇差距.
主要方法:
- 对同时出现的非识别的ICD-10代码和德语问题列表条目的日志概率分析 (约. 美国有1.9亿人).
- 统计学显著性值设置为p < 0.01,以确定种子术语.
- 与大型语言模型接口,以收获额外的任期候选人.
主要成果:
- 该方法成功地确定了额外的任期候选人.
- 绩效指标包括MAP@5的超名词 (0.801),同义词 (0.723) 和副名词 (0.507).
结论:
- 重新使用注释的临床数据集与大型语言模型是一个有效的策略.
- 这种方法通过结合现实世界的语言来提高临床术语的完整性.
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