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BIcenter-AD: Harmonising Alzheimer’s Disease cohorts using a common ETL tool

Authors :
João Rafael Almeida
Alejandro Pazos
José Luís Oliveira
Source :
Informatics in Medicine Unlocked, Vol 35, Iss , Pp 101133- (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

Background:: Many scientific studies have sought to obtain a better understanding of specific medical conditions. Concerning Alzheimer’s Disease, there is a lack of reliable diagnostics and this can be related to the availability of only small-scale ongoing biomarker studies and longitudinal cohorts including these subjects. Aiming to generate more substantial clinical evidence, researchers have started to perform multiple cohort analyses. While this is currently possible by harmonising these cohorts into a common data model, the migration pipelines are usually implemented using programming languages. Therefore, cohort owners may have difficulties contributing during the validation stage of these pipelines. Results:: To reduce the dependency on technical teams’ support when validating the data transformations, it is proposed the use of an ETL tool with visual features. BIcenter is a collaborative web platform designed to implement ETL tasks through the browser. These pipelines are constructed using drag-and-drop features and intuitive forms to customise the ETL steps. This tool is an open-source project and is accessible at https://bioinformatics-ua.github.io/BIcenter-AD/. Conclusions:: Our methodology produces interoperable cohorts for multicentric disease-specific studies. Therefore, the tool was validated using Alzheimer’s Disease cohorts from several countries, combining at the end 6,669 subjects and 172 medical attributes. The harmonised cohorts now enable multi-cohort querying and analysis, helping in the execution of new studies.

Details

Language :
English
ISSN :
23529148
Volume :
35
Issue :
101133-
Database :
Directory of Open Access Journals
Journal :
Informatics in Medicine Unlocked
Publication Type :
Academic Journal
Accession number :
edsdoj.66e125c457e74a8bb73b18c8f69767e9
Document Type :
article
Full Text :
https://doi.org/10.1016/j.imu.2022.101133