Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media

Déguilhem, Amélia; Malaab, Joelle; Talmatkadi, Manissa; Renner, Simon; Foulquié, Pierre; Fagherazzi, Guy; Loussikian, Paul; Marty, Tom; Mebarki, Adel; Texier, Nathalie; Schück, Stéphane

doi:10.2196/39849

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…Also, we can observe that BERT models are adopted for most of the different types of textual data, i.e., tweets (Miao et al, 2022 ), clinical notes (Zhu et al, 2022 ) and blogs (Scarpino et al, 2022 ). In terms of topic modeling, the approach introduced by Scarpino et al ( 2022 ) using LDA and BERT models, and the approach introduced by Déguilhem et al ( 2022 ) using Biterm Topic Modeling, adopted textual data based on patients' opinions, i.e., blogs and tweets.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the approaches using models different from BERT, they are mostly employed for identifying Long COVID symptoms [see Matharaarachchi et al ( 2022 ) using Association Rule Mining, Wang et al ( 2022 ) using PASCLex (NLP) model, and Banda et al ( 2021 ) using NLP and SVM model] and for capturing symptom co-occurrences [see Déguilhem et al ( 2022 ) using Biterm Topic Modeling].…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, Déguilhem et al ( 2022 ) collected and analyzed data from France on Long COVID most frequently reported symptoms, symptom combinations, challenges, and patient profiles. Data were gathered from the social media Twitter and the health-related online forum Doctissimo ( https://www.doctissimo.fr ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Déguilhem et al ( 2022 ) detect and study Long COVID symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles. Data were extracted based on a collection of pertinent keywords from public sites (e.g., Twitter) and health-related forums (e.g., Doctissimo) between January 2020 and August 2021.…”

Section: Literature Reviewmentioning

confidence: 99%

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A survey on the role of artificial intelligence in managing Long COVID

Ahmad,

Amelio,

Merla

et al. 2024

Front. Artif. Intell.

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In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A survey on the role of artificial intelligence in managing Long COVID

Ahmad,

Amelio,

Merla

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

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“…Callard and Peregov [48] reviewed how, through social platforms such as Twitter, patients made the persistence and heterogeneity of COVID-19 symptoms visible, thus catapulting the inclusion of post-COVID-19 condition as a relevant phenomenon in clinical and policy debates. In contrast, other authors in the last 2 years have explored on various platforms (including Twitter) the persistence of symptoms and emotional impact after months of suspected and confirmed diagnosis of COVID-19 [49][50][51][52][53][54][55], including the period of vaccination. Furthermore, others have explored web discussions regarding this phenomenon [56].…”

Section: Findings In Relation To Other Studiesmentioning

confidence: 99%

Twitter Analysis of Health Care Workers’ Sentiment and Discourse Regarding Post–COVID-19 Condition in Children and Young People: Mixed Methods Study

Chepo,

Martin,

Déom

et al. 2024

J Med Internet Res

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Background The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post–COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post–COVID-19 condition on this demographic. Objective With a social media analysis of the discourse surrounding the prevalence of post–COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post–COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post–COVID-19 condition and identify critical areas and future directions for researchers and policy makers. Methods From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post–COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs. Results We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post–COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences. Conclusions The perceptions described on Twitter by HCWs concerning the presence of the post–COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post–COVID-19 condition.

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The Long or the Post of It? Temporality, Suffering, and Uncertainty in Narratives Following COVID-19

Cheston,

Cenedese,

Woods

2023

J Med Humanit

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Long COVID affects millions of individuals worldwide but remains poorly understood and contested. This article turns to accounts of patients’ experiences to ask: What might narrative be doing both to long COVID and for those who live with the condition? What particular narrative strategies were present in 2020, as millions of people became ill, en masse, with a novel virus, which have prevailed three years after the first lockdowns? And what can this tell us about illness and narrative and about the importance of literary critical approaches to the topic in a digital, post-pandemic age? Through a close reading of journalist Lucy Adams’s autobiographical accounts of long COVID, this article explores the interplay between individual illness narratives and the collective narrativizing (or making) of an illness. Our focus on temporality and suffering knits together the phenomenological and the social with the aim of opening up Adams’s narrative and ascertaining a deeper understanding of what it means to live with the condition. Finally, we look to the stories currently circulating around long COVID and consider how illness narratives—and open, curious, patient-centered approaches to them—might shape medicine, patient involvement, and critical medical humanities research.

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Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media

Cited by 10 publications

References 43 publications

A survey on the role of artificial intelligence in managing Long COVID

A survey on the role of artificial intelligence in managing Long COVID

Twitter Analysis of Health Care Workers’ Sentiment and Discourse Regarding Post–COVID-19 Condition in Children and Young People: Mixed Methods Study

The Long or the Post of It? Temporality, Suffering, and Uncertainty in Narratives Following COVID-19

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