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儿童数据:数据驱动的儿童多中心研究的隐私保护框架

Gorkem Yilmaz1, Jonathan M Mang2, Markus Metzler1

  • 1Department of Pediatrics and Adolescent Medicine, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

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概括
此摘要是机器生成的。

一个名为PED-DATA的新模块可以从多个中心分散分析儿科数据, 建立精确的参考间隔, 同时确保数据隐私. 这种工具对于推进儿科研究和临床护理至关重要.

关键词:
数据匿名化在医疗方面多中心研究儿童医学参考价值软件

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科学领域:

  • 医疗信息学
  • 儿童医学研究
  • 数据科学

背景情况:

  • 临床数据库的数据驱动分析提供了高效的知识生成,特别是对于具有伦理和实践限制的儿科研究.
  • 多中心PEDREF 2.0研究旨在利用来自德国20多个高等保健中心的数据建立儿科参考间隔.
  • 现有的框架要求为分发儿科分析提供定制模块,以满足特定的研究需求.

研究的目的:

  • 开发和实施一个保护隐私的模块,用于对儿科数据进行分散分析.
  • 通过多中心数据协作,方便建立精确的儿科参考间隔.
  • 在分布式研究环境中解决儿科数据分析的独特挑战.

主要方法:

  • 开发和部署儿科分布式分析,匿名化和聚合模块 (PED-DATA).
  • PED-DATA是一个容器化应用程序,可实现分散的数据转换,匿名化和分析.
  • 该模块确保分布式多中心研究符合数据保护法规.

主要成果:

  • 15个中心的数据初步分析涉及超过5200万名实验室测试结果的75万多名患者.
  • 确立了前所未有的儿科参考间隔.
  • 展示了以分散的方式分析大规模儿科数据集的能力.

结论:

  • 通过PED-DATA实现了尊重隐私的,分散的多中心儿科研究.
  • 该模块在PEDREF 2.0研究中的成功实施证实了其在现实世界中的实用性.
  • 通过安全的大规模数据分析,促进儿科研究的进步.