将纵向心理健康数据纳入分期数据库:在 INSPIRE 网络 Datahub 中利用 DDI-lifecycle 和 OMOP 词汇表。

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers in Big Data Pub Date : 2024-10-11 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1435510
Bylhah Mugotitsa, Tathagata Bhattacharjee, Michael Ochola, Dorothy Mailosi, David Amadi, Pauline Andeso, Joseph Kuria, Reinpeter Momanyi, Evans Omondi, Dan Kajungu, Jim Todd, Agnes Kiragga, Jay Greenfield
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引用次数: 0

摘要

背景:纵向研究对于了解心理健康疾病随时间的发展至关重要,但将通过不同方法收集的数据结合起来以评估抑郁症、焦虑症和精神病等疾病却面临着巨大的挑战。本研究提出了一种映射技术,可将不同的纵向数据转换为标准化的分期数据库,利用数据文档倡议(DDI)生命周期和观察性医疗结果合作组织(OMOP)通用数据模型(CDM)标准,确保不同数据集之间的一致性和兼容性:方法:"INSPIRE "项目采用雪花模式结构的元数据文件标准,将非洲研究的纵向数据整合到一个分期数据库中。这有助于开发提取、转换和加载(ETL)脚本,将数据整合到 OMOP CDM 中。分期数据库模式旨在捕捉纵向研究的动态特性,包括研究方案的变化和不同数据收集浪潮中不同工具的使用:利用这种映射方法,我们简化了将数据迁移到分期数据库的过程,从而可以将其整合到 OMOP CDM 中。遵守元数据标准可确保数据质量,促进互操作性,并扩大心理健康研究数据共享的机会:分阶段数据库是管理纵向心理健康数据的创新工具,它不仅是简单的数据托管,还是全面的研究描述符。它提供了对每个研究阶段的详细了解,并为将数据标准化并整合到 OMOP CDM 中奠定了数据科学基础。
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Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub.

Background: Longitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant challenges. This study presents a mapping technique allowing for the conversion of diverse longitudinal data into a standardized staging database, leveraging the Data Documentation Initiative (DDI) Lifecycle and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standards to ensure consistency and compatibility across datasets.

Methods: The "INSPIRE" project integrates longitudinal data from African studies into a staging database using metadata documentation standards structured with a snowflake schema. This facilitates the development of Extraction, Transformation, and Loading (ETL) scripts for integrating data into OMOP CDM. The staging database schema is designed to capture the dynamic nature of longitudinal studies, including changes in research protocols and the use of different instruments across data collection waves.

Results: Utilizing this mapping method, we streamlined the data migration process to the staging database, enabling subsequent integration into the OMOP CDM. Adherence to metadata standards ensures data quality, promotes interoperability, and expands opportunities for data sharing in mental health research.

Conclusion: The staging database serves as an innovative tool in managing longitudinal mental health data, going beyond simple data hosting to act as a comprehensive study descriptor. It provides detailed insights into each study stage and establishes a data science foundation for standardizing and integrating the data into OMOP CDM.

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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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