Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data.
Antiviral Agents
/ therapeutic use
Betacoronavirus
COVID-19
Coronavirus Infections
/ epidemiology
Cough
/ physiopathology
Data Collection
Diarrhea
/ physiopathology
Disease Outbreaks
Dyspnea
/ physiopathology
Fatigue
/ physiopathology
Fever
/ physiopathology
Headache
/ physiopathology
Humans
Machine Learning
Myalgia
/ physiopathology
Oxygen Inhalation Therapy
Pandemics
Pneumonia, Viral
/ epidemiology
Publications
Retrospective Studies
Risk Factors
SARS-CoV-2
Social Media
COVID-19
SARS-CoV-2
Twitter
biomedical literature
diagnosis
risk factor
social media
symptom
treatment
tweets
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
02 10 2020
02 10 2020
Historique:
received:
20
05
2020
accepted:
13
09
2020
revised:
08
09
2020
pubmed:
17
9
2020
medline:
10
10
2020
entrez:
16
9
2020
Statut:
epublish
Résumé
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. 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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 32936770
pii: v22i10e20509
doi: 10.2196/20509
pmc: PMC7537723
doi:
Substances chimiques
Antiviral Agents
0
Types de publication
Journal Article
Meta-Analysis
Langues
eng
Sous-ensembles de citation
IM
Pagination
e20509Informations de copyright
©Jouhyun Jeon, Gaurav Baruah, Sarah Sarabadani, Adam Palanica. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.10.2020.
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