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如何为罕见疾病定制常用数据模型:基于OMOP的实施和经验教训
Najia Ahmadi1, Michele Zoch2, Oya Guengoeze3
1Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany. najia.ahmadi@tu-dresden.de.
Orphanet journal of rare diseases
|August 14, 2024
概括
开发一种罕见疾病共同数据模型 (RD-CDM) 通过协调数据来促进多中心研究. 这种基于OMOP的结构使得用于罕见疾病研究的AI驱动的洞察力成为可能.
科学领域:
- 医疗信息学 医疗信息学
- 罕见疾病研究研究 罕见疾病研究
- 数据协调与统一
背景情况:
- 罕见疾病 (RDs) 的地理稀疏性使队列组装复杂化.
- 共同数据模型 (CDM) 对于协调不同的数据源至关重要.
- 这项研究旨在支持大规模的,多中心的R&D研究.
研究的目的:
- 为罕见疾病 (RDs) 设计一个共同的数据模型 (CDM).
- 促进多中心研究和基于人工智能的R&D研究.
- 为将CDM扩展到其他领域创建蓝图.
主要方法:
- 一个跨学科的小组定义了三个医学领域的 RD 数据元素.
- 提取,转换,加载 (ETL) 过程是为了将数据映射到一个共同的CDM.
- 开发的CDM与观察医学结果伙伴关系 (OMOP) CDM进行了映射,并使用血液学数据集进行了测试.
主要成果:
- 开发并测试了一种基于OMOP的罕见病常见数据模型 (RD-CDM).
- 研究队列包括61,697名患者.
- 利用ETL流程进行无数据传输,并为表型和基因型提供特定的模块.
结论:
- 模块化RD-CDM可以扩展到新型数据类型,并作为其他领域的蓝图.
- 跨学科的利益相关者团体对于全面的CDM开发至关重要.
- 该RD-CDM能够进行大规模的多中心研究,并利用OHDSI社区工具.
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