由EHR引发的临床注释的上下文变化 迁移
Kurt Miller1,2, Sungrim Moon1, Sunyang Fu1
1Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
临床笔记结构在电子健康记录 (EHR) 系统和医疗专业之间有很大差异. 这种异质性影响自然语言处理 (NLP) 模型的通用性,需要专业特定的语言模型来提高性能.
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.
- 临床文档 临床文档
背景情况:
- 临床笔记在电子健康记录 (EHR) 系统,机构和地理位置之间表现出相当大的结构和语义变化.
- 这种异质性对在临床研究和实践中使用的便携式自然语言处理 (NLP) 模型的开发和部署构成了重大挑战.
- 了解这种变化的来源对于提高NLP在医疗保健中的应用可靠性和普遍性至关重要.
研究的目的:
- 量化临床笔记中的语义和语法变化.
- 评估电子健康记录 (EHR) 系统背景和医疗专业对临床笔记语言的影响.
- 评估空间背景 (不同地点) 对临床笔记相似性的影响.
主要方法:
- 对四个医学专业的医生的临床笔记进行比较分析.
- 测量不同电子健康记录 (EHR) 系统和网站的笔记之间的语义和语法相似性.
- 利用NLP技术来评估多地点,多专业队列中的语言变异.
主要成果:
- 发现了显著的语义和语法差异,主要是由于电子健康记录 (EHR) 系统的背景和医疗专业之间的差异.
- 在不同地点归因于空间背景的变化被发现是微小的.
- 该研究强调,EHR系统和医疗专业是临床笔记语言变化的关键决定因素.
结论:
- 临床语言模型必须调整以考虑每个医学专业的特定子语言层面的过程差异.
- 在不同临床环境和专业中NLP模型的普遍性需要解决专业特定的语言模式.
- 未来的NLP模型开发应侧重于专业级的适应,以克服临床文档中的异质性.
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