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Smart Triage Jinja Data De-identification Mawji, Alishah

Description

This dataset contains de-identified data with an accompanying data dictionary and the R script for de-identification procedures.; <strong>Objective(s):</strong> To demonstrate application of a risk based de-identification framework using the Smart Triage dataset as a clinical example. <br /><strong>Data Description:</strong> This dataset contains the de-identified version of the Smart Triage Jinja dataset with the accompanying data dictionary and R script for de-identification procedures. <br /><strong>Limitations:</strong> Utility of the de-identified dataset has only been evaluated with regard to use for the development of prediction models based on a need for hospital admission. <br /><strong>Abbreviations:</strong> NA <br /><strong>Ethics Declaration:</strong> The study was reviewed by the instituational review boards at the University of British Columbia in Canada (ID: H19-02398; H20-00484), The Makerere University School of Public Health in Uganda and the Uganda National Council for Science and Technology

Item Metadata

Title
Smart Triage Jinja Data De-identification
Creator
Mawji, Alishah
Contributor
Pallot , Katija
Date Issued
2022-03-18
Description
This dataset contains de-identified data with an accompanying data dictionary and the R script for de-identification procedures.; <strong>Objective(s):</strong> To demonstrate application of a risk based de-identification framework using the Smart Triage dataset as a clinical example. <br /><strong>Data Description:</strong> This dataset contains the de-identified version of the Smart Triage Jinja dataset with the accompanying data dictionary and R script for de-identification procedures. <br /><strong>Limitations:</strong> Utility of the de-identified dataset has only been evaluated with regard to use for the development of prediction models based on a need for hospital admission. <br /><strong>Abbreviations:</strong> NA <br /><strong>Ethics Declaration:</strong> The study was reviewed by the instituational review boards at the University of British Columbia in Canada (ID: H19-02398; H20-00484), The Makerere University School of Public Health in Uganda and the Uganda National Council for Science and Technology
Subject
Medicine, Health and Life Sciences; Common data elements; Data science; Infection; Pediatric; Infant; Data curation; Data management; Big data; Sepsis; Child; Medical informatics; Algorithms; Data aggregation
Type
Dataset
Language
English
Date Available
2022-02-09
Provider
University of British Columbia Library
License
CC BY-NC-SA 4.0
DOI
10.14288/1.0407254
URI
https://doi.org/10.5683/SP3/MSTH98
Publisher DOI
https://doi.org/10.5683/SP3/MSTH98
Rights URI
http://creativecommons.org/licenses/by-nc-sa/4.0
Aggregated Source Repository
Dataverse

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CC BY-NC-SA 4.0