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  • 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.

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人工智能的人工智能是人工智能.一个共同的数据模型.数据标准化数据标准化基因型和表型的基因型和表型.互操作性 互操作性 互操作性多中心研究研究多中心研究.这是OHDSI的OHDSI.这就是OMOP的OMOP.罕见疾病是一种罕见的疾病.

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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.