SemOntoMap:一种混合方法用于临床文本的语义注释
Ons Aouina1, Jacques Hilbey1,2, Jean Charlet1,2
1Sorbonne Université, Inserm, Université Sorbonne Paris-Nord, LIMICS, Paris, France.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究引入了一个无监督学习系统,用于解释电子健康记录 (EHR) 中的临床笔记. 这种方法通过提取实体和关系来提高研究和医疗保健的数据可用性,特别是用于精神病学应用.
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.
- 计算精神病学是一种计算精神病学.
背景情况:
- 电子健康记录 (EHR) 包含有价值的自由文本数据用于研究.
- 由于非结构化的语言,对临床笔记的自动解释具有挑战性.
- 语义注释方法在特定领域的本体学方面遇到了困难,特别是在精神病学中.
研究的目的:
- 开发一个机器可解释的EHR自由文本系统.
- 为解决精神病学数据的语义注释方面的挑战.
- 使用无监督学习提取与域本体学一致的实体和关系.
主要方法:
- 使用无监督学习技术来提取实体和关系.
- 开发了一个系统,将提取的信息与域本体学对齐.
- 在PsyCARE项目中对60名患者出院摘要验证了系统.
主要成果:
- 从精神病电子病历数据中成功提取了实体和关系.
- 证明了该系统在使临床笔记能够被机器解释方面的有效性.
- 验证了对现实世界患者出院摘要的方法.
结论:
- 无监督学习为精神病EHR数据的语义注释提供了一个可行的解决方案.
- 拟议的系统增强了临床笔记对于研究和医疗保健改进的实用性.
- 这种方法克服了专业领域传统语义注释的局限性.
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