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A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data.

Authors :
Breger, Anna
Selby, Ian
Roberts, Michael
Babar, Judith
Gkrania-Klotsas, Effrossyni
Preller, Jacobus
Escudero Sánchez, Lorena
AIX-COVNET Collaboration
Dittmer, Sören
Thorpe, Matthew
Gilbey, Julian
Korhonen, Anna
Jefferson, Emily
Langs, Georg
Yang, Guang
Xing, Xiaodan
Nan, Yang
Li, Ming
Prosch, Helmut
Jan Stanczuk
Source :
Scientific Data; 7/27/2023, Vol. 10 Issue 1, p1-16, 16p
Publication Year :
2023

Abstract

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20524463
Volume :
10
Issue :
1
Database :
Complementary Index
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
167362306
Full Text :
https://doi.org/10.1038/s41597-023-02340-7