SynthesisJournal of medical Internet research2020
Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data.
Synthesis in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 39 citations in OpenAlex.
- Surveillance of Twitter Data on COVID-19 Symptoms During the Omicron Variant Period: A Sentiment Analysis.JMIR formative research · 2025Article
- Extracting Symptoms of Complex Conditions From Online Discourse (Subreddit to Symptomatology): Lexicon-Based Approach.JMIR medical informatics · 2025Article
- Identifying COVID-19 cases and extracting patient reported symptoms from Reddit using natural language processing.Scientific reports · 2023Article
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- Article
- The prevalence of symptoms and its correlation with sex in polish COVID-19 adult patients: Cross-sectional online open survey.Frontiers in medicine · 2023Article
- Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach.Phenomics (Cham, Switzerland) · 2022Article
- Longitudinal Changes of COVID-19 Symptoms in Social Media: Observational Study.Journal of medical Internet research · 2022Observational
- Smoking increases the risk of post-acute COVID-19 syndrome: Results from a French community-based survey.Tobacco induced diseases · 2022Article
- The Impact of Public Health Events on COVID-19 Vaccine Hesitancy on Chinese Social Media: National Infoveillance Study.JMIR public health and surveillance · 2021Article
- Thyroid and COVID-19: a review on pathophysiological, clinical and organizational aspects.Journal of endocrinological investigation · 2021Review
- COVID-19 pandemic and reasons to prioritize the needs of the health care system to ensure its sustainability: A scoping review from January to October 2020 (Review).Experimental and therapeutic medicine · 2021Article
- Learning From the Experiences of COVID-19 Survivors: Web-Based Survey Study.JMIR formative research · 2021Article
- Google Trends for Pain Search Terms in the World's Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study.Journal of medical Internet research · 2021Article
- Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.Journal of medical Internet research · 2021Article
- A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
objectiveThis 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.
methodsThrough 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.
resultsFrom 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.
conclusionsIdentified 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.