1. Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS
- Author
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Rashidi, Hooman H, Pepper, John, Howard, Taylor, Klein, Karina, May, Larissa, Albahra, Samer, Phinney, Brett, Salemi, Michelle R, and Tran, Nam K
- Subjects
Analytical Chemistry ,Chemical Sciences ,Vaccine Related ,Emerging Infectious Diseases ,Lung ,Prevention ,Infectious Diseases ,Bioengineering ,Biodefense ,Good Health and Well Being ,COVID-19 ,COVID-19 Testing ,Clinical Laboratory Techniques ,Humans ,Machine Learning ,SARS-CoV-2 ,Spectrometry ,Mass ,Matrix-Assisted Laser Desorption-Ionization ,General Science & Technology - Abstract
The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method's robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.
- Published
- 2022