COVID-Net Biochem: an explainability-driven framework to building machine learning models for predicting survival and kidney injury of COVID-19 patients from clinical and biochemistry data

• Published in: Scientific reports Vol. 13; no. 1; pp. 17001 - 14
• Main Authors: Aboutalebi, Hossein, Pavlova, Maya, Shafiee, Mohammad Javad, Florea, Adrian, Hryniowski, Andrew, Wong, Alexander
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.10.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/45; 692/308/575; Biochemical markers; Catalysts; COVID-19; Datasets; Decision making; Design; Disease transmission; Humanities and Social Sciences; Kidneys; Learning algorithms; Machine learning; multidisciplinary; Pandemics; Patients; Predictions; Recombinants; Resource allocation; Science; Science (multidisciplinary); Scientists; Severe acute respiratory syndrome coronavirus 2; Survival
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-42203-0

Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SARS-CoV-2 virus, as well as its evolving subvariants and recombinants. A significant challenge during the pandemic has no...