What is it about?
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has made clear the importance of correctly classifying antibody test results. We show that using additional antigens, which corresponds to viewing the data in higher dimensions, allows us to outperform the accuracy of traditional classification methods and previous results. This is achieved via mathematical modeling and optimal decision theory.
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Why is it important?
High dimensional mathematical modeling is a powerful tool for classification, can achieve superior accuracy, and has the potential to replace traditional methods. A significant benefit of our modeling approach is its adaptability to any number of measurement targets and classes. Our classification scheme can also be rewritten to meet desired sensitivity and specificity targets.
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This page is a summary of: Modeling in higher dimensions to improve diagnostic testing accuracy: Theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays, PLoS ONE, March 2023, PLOS,
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