Open access
Date
2023-06-15Type
- Journal Article
ETH Bibliography
yes
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Abstract
Objective: To develop a unified framework for analyzing data from 5 large publicly available intensive care unit (ICU) datasets. Findings: Using 3 American (Medical Information Mart for Intensive Care III, Medical Information Mart for Intensive Care IV, electronic ICU) and 2 European (Amsterdam University Medical Center Database, High Time Resolution ICU Dataset) databases, we constructed a mapping for each database to a set of clinically relevant concepts, which are grounded in the Observational Medical Outcomes Partnership Vocabulary wherever possible. Furthermore, we performed synchronization in the units of measurement and data type representation. On top of this, we built functionality, which allows the user to download, set up, and load data from all of the 5 databases, through a unified Application Programming Interface. The resulting ricu R-package represents the computational infrastructure for handling publicly available ICU datasets, and its latest release allows the user to load 119 existing clinical concepts from the 5 data sources. Conclusion: The ricu R-package (available on GitHub and CRAN) is the first tool that enables users to analyze publicly available ICU datasets simultaneously (datasets are available upon request from respective owners). Such an interface saves researchers time when analyzing ICU data and helps reproducibility. We hope that ricu can become a community-wide effort, so that data harmonization is not repeated by each research group separately. One current limitation is that concepts were added on a case-to-case basis, and therefore the resulting dictionary of concepts is not comprehensive. Further work is needed to make the dictionary comprehensive. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000627712Publication status
publishedExternal links
Journal / series
GigaScienceVolume
Pages / Article No.
Publisher
Oxford University PressSubject
Intensive care medicine; electronic health records; Computational methodsOrganisational unit
03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
03990 - Meinshausen, Nicolai / Meinshausen, Nicolai
Funding
786461 - Statistics, Prediction and Causality for Large-Scale Data (EC)
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ETH Bibliography
yes
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