通过启用模型的数据管理实现集成数据语义
Integrative Data Semantics through a Model-enabled Data Stewardship
摘要：
动机：临床数据在理解复杂疾病的病理生理学方面的重要性促使了旨在从各种模式生成患者水平数据的多项举措的推出。虽然这些研究可以揭示与疾病相关的重要发现，但每项研究都涵盖了不同但互补的方面和模式，当结合起来时，可以更全面地了解疾病的病因。然而，实现这一目标需要跨研究的数据的全球整合，鉴于缺乏队列数据集的互操作性，这被证明是具有挑战性的。结果如下：在这里，我们提出了数据管家工具（DST），一个应用程序，允许半自动语义集成的临床数据到本体和全球数据模型和数据标准。我们证明了该工具在痴呆症研究领域的适用性，通过建立一个临床数据模型（CDM）在这一领域。CDM目前包括277个常见变量，涵盖人口统计学（例如年龄和性别）、诊断、神经心理学测试和生物标志物测量。DST与这种疾病特定的数据模型相结合，展示了如何实现多个异构痴呆数据集之间的互操作性。
英文摘要：
Motivation: The importance of clinical data in understanding the pathophysiology of complex disorders has prompted the launch of multiple initiatives designed to generate patient-level data from various modalities. While these studies can reveal important findings relevant to the disease, each study captures different yet complementary aspects and modalities which, when combined, generate a more comprehensive picture of disease aetiology. However, achieving this requires a global integration of data across studies, which proves to be challenging given the lack of interoperability of cohort datasets. Results: Here, we present the Data Steward Tool (DST), an application that allows for semi-automatic semantic integration of clinical data into ontologies and global data models and data standards. We demonstrate the applicability of the tool in the field of dementia research by establishing a Clinical Data Model (CDM) in this domain. The CDM currently consists of 277 common variables covering demographics (e.g. age and gender), diagnostics, neuropsychological tests, and biomarker measurements. The DST combined with this disease-specific data model shows how interoperability between multiple, heterogeneous dementia datasets can be achieved.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
