收购一个词典的家族史信息:双向编码器表示从变压器辅助子语言分析
Liwei Wang1, Huan He1, Andrew Wen1
1Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.
JMIR medical informatics
|June 27, 2023
概括
这项研究开发了一个家族史 (FH) 词汇资源和基于规则的系统,以从临床笔记中提取FH信息. 这提高了临床护理和研究的数据可用性.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 生物医学数据科学 生物医学数据科学
背景情况:
- 家庭病史 (FH) 对于临床护理至关重要,但在电子健康记录中不一致地被捕获.
- 在临床笔记中,FH数据通常是无结构的,这阻碍了其在分析和决策支持中的使用.
- 自然语言处理 (NLP) 为提取和规范化FH信息提供了一个解决方案.
研究的目的:
- 为信息提取和规范化构建一个全面的FH词汇资源.
- 开发和评估用于FH数据提取的NLP系统.
主要方法:
- 使用基于变压器的方法,从初级保健临床笔记中构建FH词汇资源.
- 开发了一个基于规则的FH系统来提取FH实体和关系.
- 此外,还实施并评估了基于深度学习的FH系统.
- 使用已建立的FH挑战数据集来评估性能.
主要成果:
- FH词典包含33,603个条目,标准化为6,408个UMLS和15,126个SNOMED CT代码.
- 基于规则的系统在FH提取中表现出了合理的性能.
- 结合基于规则和深度学习系统,改善了FH信息提取的回忆.
结论:
- 创建了一个有价值的FH词汇资源和基于规则的系统.
- 这些资源通过GitHub可用,以方便FH信息的提取和利用.
- 这些发现支持通过更好的FH数据可访问性来改善临床决策.
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