Machine learning identifies risk factors associated with long-term sick leave following COVID-19 in Danish population.

Kim Daniel Jakobsen Elisabeth O'ReganIngrid Bech SvalgaardAnders Hviid

Published in: Communications medicine (2023)

Our study supports significant individual-level heterogeneity in the effect of SARS-CoV-2 infection on long-term sick-leave, with age, sex, high BMI, and depression identified as key factors. Efforts to curb the PCC burden should consider multimorbidity and individual-level risk.

Keyphrases

machine learning
coronavirus disease
sars cov
body mass index
respiratory syndrome coronavirus
depressive symptoms
single cell
quality improvement
artificial intelligence
weight gain
risk factors
sleep quality
dna methylation