tetano
Editor, Senior Moderator
J Med Internet Res
. 2020 Sep 13.
doi: 10.2196/20509. Online ahead of print.
Identification of risk factors and symptoms of SARS-CoV-2 (COVID-19) using biomedical literature and social media data: Integrative and Consensus study
Jouhyun Jeon[SUP] 1 [/SUP], Gaurav Baruah[SUP] 1 [/SUP], Sarah Sarabadani[SUP] 1 [/SUP], Adam Palanica[SUP] 1 [/SUP]
Affiliations
Abstract
Background: In December 2019, Coronavirus disease 2019 (COVID-19) outbreak started in China and rapidly spread around the world. Lack of any vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of COVID-19 patients.
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 various outcomes of COVID-19 patients.
Methods: Through semantic analysis, we collected 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of COVID-19 patients, and 84,140 Twitter posts from 1,036 COVID-19 positive users. Machine-learning tools to extract biomedical information were introduced to identify uncommon or novel symptoms mentioning in social media. We then examined and compared two datasets 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 social media 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 COVID-19 patients and predict their clinical outcomes providing appropriate treatments.
. 2020 Sep 13.
doi: 10.2196/20509. Online ahead of print.
Identification of risk factors and symptoms of SARS-CoV-2 (COVID-19) using biomedical literature and social media data: Integrative and Consensus study
Jouhyun Jeon[SUP] 1 [/SUP], Gaurav Baruah[SUP] 1 [/SUP], Sarah Sarabadani[SUP] 1 [/SUP], Adam Palanica[SUP] 1 [/SUP]
Affiliations
- PMID: 32936770
- DOI: 10.2196/20509
Abstract
Background: In December 2019, Coronavirus disease 2019 (COVID-19) outbreak started in China and rapidly spread around the world. Lack of any vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of COVID-19 patients.
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 various outcomes of COVID-19 patients.
Methods: Through semantic analysis, we collected 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of COVID-19 patients, and 84,140 Twitter posts from 1,036 COVID-19 positive users. Machine-learning tools to extract biomedical information were introduced to identify uncommon or novel symptoms mentioning in social media. We then examined and compared two datasets 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 social media 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 COVID-19 patients and predict their clinical outcomes providing appropriate treatments.