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Since the World Health Organization declared COVID-19 a pandemic in 2020, theglobal community has faced ongoing challenges in controlling and mitigating thetransmission of the SARS-CoV-2 virus, as well as its evolving subvariants andrecombinants. A significant challenge during the pandemic has not only been theaccurate detection of positive cases but also the efficient prediction of risksassociated with complications and patient survival probabilities. These tasksentail considerable clinical resource allocation and attention.In this study,we introduce COVID-Net Biochem, a versatile and explainable framework forconstructing machine learning models. We apply this framework to predictCOVID-19 patient survival and the likelihood of developing Acute Kidney Injuryduring hospitalization, utilizing clinical and biochemical data in atransparent, systematic approach. The proposed approach advances machinelearning model design by seamlessly integrating domain expertise withexplainability tools, enabling model decisions to be based on key biomarkers.This fosters a more transparent and interpretable decision-making process madeby machines specifically for medical applications.

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