Oral manifestations in patients with coronavirus disease 2019 (COVID-19) identified using text mining: an observational study.
Sci Rep
; 13(1): 17770, 2023 10 18.
Article
em En
| MEDLINE
| ID: mdl-37853031
Text mining enables search, extraction, categorisation and information visualisation. This study aimed to identify oral manifestations in patients with COVID-19 using text mining to facilitate extracting relevant clinical information from a large set of publications. A list of publications from the open-access COVID-19 Open Research Dataset was downloaded using keywords related to oral health and dentistry. A total of 694,366 documents were retrieved. Filtering the articles using text mining yielded 1,554 oral health/dentistry papers. The list of articles was classified into five topics after applying a Latent Dirichlet Allocation (LDA) model. This classification was compared to the author's classification which yielded 17 categories. After a full-text review of articles in the category "Oral manifestations in patients with COVID-19", eight papers were selected to extract data. The most frequent oral manifestations were xerostomia (n = 405, 17.8%) and mouth pain or swelling (n = 289, 12.7%). These oral manifestations in patients with COVID-19 must be considered with other symptoms to diminish the risk of dentist-patient infection.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Envio de Mensagens de Texto
/
COVID-19
Limite:
Humans
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Colômbia
País de publicação:
Reino Unido