Abstract
AbstractThe European project ORCHESTRA intends to create a new pan-European cohort to rapidly advance the knowledge of the effects and treatment of COVID-19. Establishing processes that facilitate the merging of heterogeneous clusters of retrospective data was an essential challenge. In addition, data from new ORCHESTRA prospective studies have to be compatible with earlier collected information to be efficiently combined. In this article, we describe how we utilized and contributed to existing standard terminologies to create consistent semantic representation of over 2500 COVID-19-related variables taken from three ORCHESTRA studies. The goal is to enable the semantic interoperability of data within the existing project studies and to create a common basis of standardized elements available for the design of new COVID-19 studies. We also identified 743 variables that were commonly used in two of the three prospective ORCHESTRA studies and can therefore be directly combined for analysis purposes. Additionally, we actively contributed to global interoperability by submitting new concept requests to the terminology Standards Development Organizations.
Funder
EC | Horizon 2020 Framework Programme
Publisher
Springer Science and Business Media LLC
Subject
Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)
Reference60 articles.
1. IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. IEEE Std 610 1–217 (1991) https://doi.org/10.1109/IEEESTD.1991.106963.
2. Solle, D. Be FAIR to your data. Anal. Bioanal. Chem. 412, 3961–3965 (2020).
3. Dugas, M. et al. Portal of medical data models: information infrastructure for medical research and healthcare. Database J. Biol. Databases Curation 2016, bav121 (2016).
4. Kim, H. H., Park, Y. R., Lee, S. & Kim, J. H. Composite CDE: modeling composite relationships between common data elements for representing complex clinical data. BMC Med. Inform. Decis. Mak. 20, 147 (2020).
5. Sass, J. et al. The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond. BMC Med. Inf. Decis Mak 20, (2020).
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献