Background: In December 2019, the COVID-19 outbreak started in
China and rapidly spread around the world. Lack of a vaccine or
optimized intervention raised the importance of characterizing risk
factors and symptoms for the early identification and successful
treatment of patients with COVID-19. Objective: This study aims to
investigate and analyze biomedical literature and public social
media data to understand the association of risk factors and
symptoms with the various outcomes observed in patients with
COVID-19. Methods: Through semantic analysis, we collected 45
retrospective cohort studies, which evaluated 303 clinical and
demographic variables across 13 different outcomes of patients with
COVID-19, and 84,140 Twitter posts from 1036 COVID-19–positive
users. Machine learning tools to extract biomedical information
were introduced to identify mentions of uncommon or novel symptoms
in tweets. We then examined and compared two data sets to expand
our landscape of risk factors and symptoms related to COVID-19.
Results: From the biomedical literature, approximately 90% of
clinical and demographic variables showed inconsistent associations
with COVID-19 outcomes. Consensus analysis identified 72 risk
factors that were specifically associated with individual outcomes.
From the social media data, 51 symptoms were characterized and
analyzed. By comparing social media data with biomedical
literature, we identified 25 novel symptoms that were specifically
mentioned in tweets but have been not previously well
characterized. Furthermore, there were certain combinations of
symptoms that were frequently mentioned together in social media.
Conclusions: Identified outcome-specific risk factors, symptoms,
and combinations of symptoms may serve as surrogate indicators to
identify patients with COVID-19 and predict their clinical outcomes
in order to provide appropriate treatments.
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Identification of Risk Factors and Symptoms of COVID-19:
Analysis of Biomedical Literature and Social Media Data