优化临床数据丰富智能研究。

Michele Zoch, Christian Gierschner, Richard Gebler, Martin Sedlmayr, Ines Reinecke
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引用次数: 0

摘要

通过外部数据充实方法加强常规护理数据的二次利用,可显著提高常规护理数据的质量。本文演示了一种流程驱动的原型方法,该方法分离敏感和非敏感数据,使医学专家能够将自由文本中的医学概念映射到标准化的术语代码,同时提供数据保护和信息安全。该方法基于通过焦点小组讨论开发的面向原型的框架。它由四个组成部分组成:(A)临床数据存储库,(B)转换数据库,(C)映射工具和(D)验证工具。组件之间的数据流包含自由文本形式的医学概念和建议的或经过验证的标准代码的结构化列表。它们在提取、转换和加载过程以及工作流管理工具的帮助下进行操作。通过在整个过程中利用这些组件,可以提供有质量保证的医疗概念及其映射,以便为研究提供常规患者数据的二次使用。
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Optimizing Clinical Data Enrichment for Intelligent Research.

Enhancing the secondary use of data from routine care through external data enrichment methods can significantly boost its quality. This paper demonstrates a process-driven prototyping approach that separates sensitive and non-sensitive data, empowering medical experts to map medical concepts in free text to standardized terminology codes, all while granting data protection and information security. This approach is based on a prototype-oriented framework developed through discussions in a focus group. It consists of four integral components: (A) Clinical Data Repository, (B) Transition Database, (C) Mapping Tools and (D) Validation Tools. Data flows between the components contain medical concepts in free text and structured lists of suggested or validated standard codes. They are operated with the help of extract, transform and load processes as well as workflow management tools. By utilizing the components along the process, quality-assured medical concepts and their mapping can be provided for the secondary use of routine patient data for research.

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