A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms.

Nusrat J EpsiJohn H PowersDavid A LindholmKatrin MendeAllison MalloyAnuradha GanesanNikhil HuprikarTahaniyat LalaniAlfred SmithRupal M ModyMilissa U JonesSamantha E BazanRhonda E ColomboChristopher J ColomboEvan C EwersDerek T LarsonCatherine M Berjohn Carlos J MaldonadoPaul W BlairJosh ChenowethDavid L SaundersJeffrey LivezeyRyan C MavesMargaret Sanchez EdwardsJulia S RozmanMark P SimonsDavid R TribbleBrian K AganTimothy H BurgessSimon D Pollettnull null

Published in: PloS one (2023)

We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.

Keyphrases

coronavirus disease
sars cov
machine learning
case report
oxidative stress
genome wide
sleep quality
artificial intelligence
patient reported
respiratory syndrome coronavirus
gene expression
deep learning
dna methylation
breast cancer risk