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对结构化和非结构化电子健康记录数据的描述和比较,将其映射到MedDRA,用于营销后监测

Joshua C Smith¹, Sharon E Davis¹, Ruth M Reeves¹

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.

|March 16, 2026

概括

非结构化的电子健康记录 (EHR) 数据,当映射到监管活动医学词典 (MedDRA) 时,通过揭示仅在结构化代码中不存在的信息,显著提高了医疗产品安全监督.

科学领域:

医疗信息学医疗信息学
药物监督药物监督药物监督
自然语言处理 (NLP) 是一种自然语言处理.

背景情况:

医疗产品安全监督传统上依赖于来自电子健康记录 (EHR) 或声明的结构化数据.
非结构化的EHR数据,如临床笔记,为数据挖掘和表型化提供了更丰富的信息来源.
自然语言处理 (NLP) 技术可以从非结构化的临床文本中提取有价值的数据.

研究的目的:

评估监控的结构化和非结构化电子健康记录数据之间的重叠和不同的信息.
将结构化 (ICD-10-CM) 和非结构化数据映射到一个共同的词汇 (MedDRA).
评估可行性并探索MedDRA概念层次结构中的相似之处/差异.

主要方法:

随机抽样15000次接触 (行医,急诊,住院).
从使用MetaMap的临床笔记中提取了MedDRA概念,并将ICD-10-CM代码映射到MedDRA.
评估数据源验证和在MedDRA层次结构中的独特信息贡献.

主要成果:

处理了119,492个临床笔记,并将163,254个ICD-10-CM代码映射到MedDRA.
在MedDRA首选术语水平上,结构化和非结构化数据之间发现了显著的重叠 (73-98%).
很大一部分 (80-95%) 的 MedDRA 概念来自非结构化文本,在结构化 ICD-10-CM 代码中没有被发现.

结论:

来自结构化数据的MedDRA概念在很大程度上得到了非结构化文本的支持.
大多数在接触中发现的MedDRA概念都是非结构化的临床文本中独有的.
整合MedDRA编码的非结构化文本补充了结构化数据,以实现更全面的患者表型和药物监督.

关键词:

数据挖掘是数据挖掘的一个方法.电子健康记录是电子健康记录.疾病的国际分类.自然语言处理自然语言处理.药物监督和药物监督

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