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Characterization of long-term patient-reported symptoms of COVID-19: an analysis of social media data

Authors :
Osaid Alser
Jitendra Jonnagaddala
Vyjeyanthi S. Periyakoil
Carlos Areia
Kristina Fišter
Muath Alser
Miguel Angel Mayer
Lourdes Mateu
Waheed-Ul-Rahman Ahmed
Lana Lai
Vojtech Huser
Daniel Prieto-Alhambra
Daniel R. Morales
Elsie Gyang Ross
Heba Alghoul
Vignesh Subbian
Evan P. Minty
Gurdas V. Singh
Saurabh Gombar
Juan M. Banda
Nicola Adderley
Angela Leis
Albert Prats-Uribe
Roger Paredes
Karthik Natarajan
Arani Vivekanantham
Mikail Cogenur
Publication Year :
2021
Publisher :
Cold Spring Harbor Laboratory, 2021.

Abstract

As the SARS-CoV-2 virus (COVID-19) continues to affect people across the globe, there is limited understanding of the long term implications for infected patients1–3. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually designed by clinicians, and not granular enough to understand the natural history or patient experiences of ‘long COVID’. In order to get a complete picture, there is a need to use patient generated data to track the long-term impact of COVID-19 on recovered patients in real time. There is a growing need to meticulously characterize these patients’ experiences, from infection to months post-infection, and with highly granular patient generated data rather than clinician narratives. In this work, we present a longitudinal characterization of post-COVID-19 symptoms using social media data from Twitter. Using a combination of machine learning, natural language processing techniques, and clinician reviews, we mined 296,154 tweets to characterize the post-acute infection course of the disease, creating detailed timelines of symptoms and conditions, and analyzing their symptomatology during a period of over 150 days.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........d2d1c56ec2eeb5fe4bca47cda8ac2989
Full Text :
https://doi.org/10.1101/2021.07.13.21260449