开发一个知识图嵌入式模型的疼痛的发展
Jaya Chaturvedi1, Tao Wang1, Sumithra Velupillai1
1Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
这项研究使用来自电子健康记录和SNOMED CT的知识图嵌入式来建模疼痛概念. 这种方法通过表示复杂的关系来增强对疼痛的理解,以便更好地进行计算分析.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 知识表示 知识表示
背景情况:
- 疼痛是一种复杂的现象,有相互关联的概念,对于了解患者健康至关重要.
- 从电子健康记录 (EHR) 中建模疼痛是具有挑战性的,因为数据的复杂性.
- 知识图提供了一种结构化的方式来表示与疼痛相关的概念和关系.
研究的目的:
- 为疼痛概念构建知识图嵌入模型.
- 将非结构化心理健康电子健康记录的信息与结构化医学知识 (SNOMED CT) 整合起来.
- 在链接预测任务上评估这些模型的有效性.
主要方法:
- 从非结构化的EHR文本中提取了疼痛概念和关系.
- 使用SNOMED CT来结合外部医学知识.
- 开发和训练知识图嵌入模型.
- 使用主体对象链接预测任务评估的模型.
主要成果:
- 构建的知识图嵌入模型在表示与疼痛相关的概念及其关系方面表现出有效性.
- 对链接预测任务的基线模型进行了性能评估.
- 这些模型成功地整合了异质数据源 (EHR和SNOMED CT).
结论:
- 知识图嵌入为分析复杂的疼痛概念提供了一个计算可处理的方法.
- 将EHR数据与标准化医学术语 (如SNOMED CT) 整合起来,可以增强疼痛知识的表现.
- 这种方法有可能改善临床环境中的疼痛理解和建模.
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