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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7264| Characterization of long-term patient-reported symptoms of COVID-19: an analysis of social media data | |
| Juan Banda Nicola Adderley Waheed-Ul-Rahman Ahmed Heba Alghoul Osaid Alser Muath Alser Carlos Areia Mikail Gögenur Kristina Fišter Saurabh Gombar Vojtech Huser Jitendra Jonnagaddala Lana Lai Angela Leis Lourdes Mateu Miguel Angel Mayer Evan Minty Daniel Morales Karthik Natarajan Roger Paredes Vyjeyanthi Periyakoil Albert Prats Uribe Elsie Gyang Ross Gurdas V Singh Vignesh Subbian Arani Vivekanantham Daniel Prieto_Alhambra | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2021.07.13.21260449 | |
| https://www.medrxiv.org/content/10.1101/2021.07.13.21260449v1 | |
| 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 patients. 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. | |
| medRxiv | |
| 15-07-2021 | |
| Preimpreso | |
| www.medrxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Characterization of long term patient reported symptoms of COVID 19 an analysis of social media data.pdf | 881.42 kB | Adobe PDF | Visualizar/Abrir |