Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning.

ABSTRACT:

Objective

To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

Material and methods

Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.

Results

The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatalit

SUBMITTER: de Fatima Cobre A 

PROVIDER: S-EPMC9123826 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature