Triaging moderate COVID-19 and other viral pneumonias from routine blood tests

TL;DR

This study uses machine learning on routine blood tests to differentiate between COVID-19 and other viral pneumonias, offering a cost-effective alternative to traditional testing methods.

Contribution

It introduces a machine learning approach utilizing blood tests for COVID-19 diagnosis, with models trained and validated on clinical data, and provides an accessible web portal for medical use.

Findings

SVM classifier achieved 84% accuracy

High sensitivity of 88% for COVID-19 detection

Models are explainable and publicly available

Abstract

The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted with COVID-19 and non-COVID-19 viral pneumonia a priority and a challenge. However, COVID-19 testing has been greatly limited by the availability and cost of existing methods, even in developed countries like the US. Intrigued by the wide availability of routine blood tests, we propose to leverage them for COVID-19 testing using the power of machine learning. Two proven-robust machine learning model families, random forests (RFs) and support vector machines (SVMs), are employed to tackle the challenge. Trained on blood data from 208 moderate COVID-19 subjects and 86 subjects with non-COVID-19 moderate viral pneumonia, the best result is obtained in an SVM-based classifier with an accuracy of 84%, a sensitivity of 88%, a specificity of 80%, and a precision of 92%. The results are found explainable from both machine learning and medical perspectives. A privacy-protected web portal is set up to help medical personnel in their practice and the trained models are released for developers to further build other applications. We hope our results can help the world fight this pandemic and welcome clinical verification of our approach on larger populations.