将非结构化乳腺癌病理报告转化为观察医学结果伙伴关系共同数据模型
Borham Kim1, Wongeun Song1,2, Eunsil Yoon1
1Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
BMC medical informatics and decision making
|March 1, 2026
概括
使用自然语言处理 (NLP) 和观察医疗结果伙伴关系 (OMOP) 的共同数据模型 (CDM) 标准化乳腺癌病理报告使统一的数据分析成为可能. 这种方法在预测患者死亡率方面具有实用性,有助于回顾性瘤学研究.
科学领域:
- 生物医学信息学 生物医学信息学
- 瘤学研究研究
- 数据标准化数据标准化
背景情况:
- 乳腺癌病理学报告包含重要的临床数据,但是非结构化的自由文本.
- 以前的努力使用自然语言处理 (NLP) 来从这些报告中提取数据.
- 这项研究的重点是将提取的病理学数据标准化为观察医学结果伙伴关系 (OMOP) 共同数据模型 (CDM).
研究的目的:
- 将乳腺癌病理学数据标准化的过程介绍到OMOP CDM中.
- 为了证明这种标准化数据对回顾性瘤学研究的有用性.
- 为其他机构提供一个框架来采用.
主要方法:
- 从13,857份外科病理学,11,374份免疫组织化学和5,904份分子病理学报告中提取了数据.
- 采用基于规则的NLP,包括文本预处理,细分和基于模式的提取.
- 将提取的数据映射到OMOP标准词汇中,并集成到现有的OMOPCDM中.
主要成果:
- 从NLP衍生的病理学数据填充了OMOP表,如NOTE_NLP,CONDITION_OCCURRENCE和MEASUREMENT等.
- 一项可行性研究证明了综合数据的实用性.
- 患者层面的预测分析给出了接受器运行特征曲线 (AUROC) 下面的面积为0.840,用于5年全因死亡率预测.
结论:
- 将乳腺癌病理学数据集成到OMOP CDM中,有助于统一分析临床和病理学数据.
- 基于NLP的标准化框架可以适应其他机构.
- 这种方法支持回顾性瘤学研究和在癌症研究中扩大OMOPCDM的使用.
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