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

TL;DR

COVID-Net Biochem is an explainability-driven framework that constructs machine learning models to predict COVID-19 patient survival and kidney injury using clinical and biochemical data, emphasizing transparency and interpretability.

Contribution

The paper introduces COVID-Net Biochem, a novel framework that combines domain expertise with explainability tools for building interpretable machine learning models in healthcare.

Findings

Effective prediction of COVID-19 survival and kidney injury.

Models highlight key biomarkers influencing outcomes.

Enhanced transparency in machine learning decision-making.

Abstract

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.