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

• Published in: Scientific data Vol. 10; no. 1; p. 493
• Main Authors: Breger, Anna, Selby, Ian, Roberts, Michael, Babar, Judith, Gkrania-Klotsas, Effrossyni, Preller, Jacobus, Escudero Sánchez, Lorena, Rudd, James H. F., Aston, John A. D., Weir-McCall, Jonathan R., Sala, Evis, Schönlieb, Carola-Bibiane
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.07.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 692/53/2421; 692/53/2422; 692/53/2423; Artificial intelligence; Coronaviruses; COVID-19; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Science; Science (multidisciplinary); Thorax
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-023-02340-7

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.