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
depressive symptoms
respiratory syndrome coronavirus
genome wide
artificial intelligence
gene expression
weight gain
deep learning