“If we can determine which people can naturally avoid SARS-CoV-2 infection, we can learn which genetic and environmental differences affect their protection from the virus,” said Karen Young, lead author of the study.
Using machine learning models to recognize complex patterns in large numbers of people with COVID-19 made it possible to predict the course of a single patient’s illness in 2021 and determine the probability of transitioning to a severe form. Building on this success, the team wondered whether the same approach could be applied to predict who might be exposed to SARS-CoV-2 in a close room and not become infected.
“In the training and testing set, we identified 56 ICD code patterns divided into two groups: resistance-related or non-resistance-related,” Yang says.
He adds that future studies using more patients are needed to validate the model’s capabilities.
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