文献中的补充和整合性健康信息:它的词汇和命名实体认可
Huixue Zhou1, Robin Austin2, Sheng-Chieh Lu3
1Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States.
Journal of the American Medical Informatics Association : JAMIA
|November 12, 2023
概括
一个新的补充和整合性健康词典 (CIHLex) 改善了CIH研究方法的代表性. 先进的NLP模型,特别是BERT,在生物医学文献中识别CIH实体方面表现强.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 补充和整合性健康
背景情况:
- 标准的术语往往低于身体和心理的补充和整合性健康 (CIH) 方法.
- 在生物医学文献中准确地识别CIH概念对于研究和临床实践至关重要.
- 开发专门的词典和使用先进的NLP技术可以解决这些表现差距.
研究的目的:
- 构建一个全面的补充和整合健康词典 (CIHLex).
- 在标准术语中增强CIH方法的表现.
- 应用最先进的自然语言处理 (NLP) 技术来识别生物医学文献中的CIH概念.
主要方法:
- 从生物医学文献和知识来源编制和整合数据,以创建CIHLex.
- 将724个独特的CIH概念与统一医疗语言系统 (UMLS) 相匹配.
- 开发并将BERT模型与MetaMap,CLAMP和GPT3.5-turbo进行比较,用于CIH命名实体识别.
主要成果:
- CIHLex包含724个独特的概念;27.2%被映射到UMLS术语中,其中74.9%被归类为"治疗或预防程序".
- 在CIH命名实体认可中,BLUEBERT实现了0.91的最高宏观平均F1得分,超过了其他模型.
- 先进的NLP模型,特别是BERT,在识别CIH实体方面表现出了显著的实用性.
结论:
- 开发的CIHLex大大改善了CIH方法在生物医学文献中的表现.
- 先进的NLP模型,特别是BERT,在识别CIH实体方面表现出很高的有效性.
- 这些发现为生物医学环境中CIH术语的标准化和认可提供了有希望的策略.
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