Triaging moderate COVID-19 and other viral pneumonias from routine blood tests
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 avai...
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Main Authors | , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
13.05.2020
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2005.06546 |