  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 not only been the
accurate detection of positive cases but also the efficient prediction of risks
associated with complications and patient survival probabilities. These tasks
entail considerable clinical resource allocation and attention.In this study,
we introduce COVID-Net Biochem, a versatile and explainable framework for
constructing machine learning models. We apply this framework to predict
COVID-19 patient survival and the likelihood of developing Acute Kidney Injury
during hospitalization, utilizing clinical and biochemical data in a
transparent, systematic approach. The proposed approach advances machine
learning model design by seamlessly integrating domain expertise with
explainability tools, enabling model decisions to be based on key biomarkers.
This fosters a more transparent and interpretable decision-making process made
by machines specifically for medical applications.
