Cross-Standard Health Data Harmonization using Semantics of Data Elements.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-12-19 DOI:10.1038/s41597-024-04168-1

Shuxin Zhang, Ronald Cornet, Nirupama Benis

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引用次数: 0

Abstract

Faced with heterogeneity of healthcare data, we propose a novel approach for harmonizing data elements (i.e., attributes) across health data standards. This approach focuses on the implicit concept that is represented by a data element. The process includes the following steps: identifying concepts, clustering similar concepts and constructing mappings between the clusters using the Simple Standard for Sharing Ontological Mappings (SSSOM) and Resource Description Framework (RDF), and enabling the creation of reusable mappings. As proof-of-concept, we applied the approach to five common health data standards - HL7 FHIR, OMOP, CDISC, Phenopackets, and openEHR, across four domains, such as demographics and diagnoses, and nine topics within those domains, such as gender and vital status. These domains and topics are selected to represent the broader range of topics in the health field. For each topic, data elements were found in the health data standards after a thorough search, resulting in the analysis of 64 data elements, identification of their underlying concepts, and development of mappings. Three use cases were implemented to demonstrate the role of data element concepts in data harmonization and data querying at varying levels of granularity. The approach helps overcome the limitations of context-dependent mappings and provides valuable insight for mapping practice within the health domain.

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使用数据元素语义的跨标准健康数据协调。

面对医疗数据的异质性，我们提出了一种新的方法来协调跨医疗数据标准的数据元素（即属性）。这种方法侧重于由数据元素表示的隐式概念。该过程包括以下步骤：识别概念，将相似的概念聚类，使用共享本体映射的简单标准（SSSOM）和资源描述框架（RDF）在集群之间构建映射，并启用可重用映射的创建。作为概念验证，我们将该方法应用于五个常见的健康数据标准——HL7 FHIR、OMOP、CDISC、Phenopackets和openEHR，涉及人口统计学和诊断等四个领域，以及这些领域中的九个主题，如性别和生命状态。选择这些领域和主题是为了代表卫生领域更广泛的主题。对于每个主题，经过彻底搜索，在健康数据标准中找到了数据元素，分析了64个数据元素，确定了它们的基础概念，并开发了映射。实现了三个用例来演示数据元素概念在不同粒度级别的数据协调和数据查询中的作用。该方法有助于克服依赖于上下文的映射的局限性，并为健康领域内的映射实践提供有价值的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.