tetano
Editor, Senior Moderator
Sci Rep
. 2023 Oct 18;13(1):17770.
doi: 10.1038/s41598-023-44784-2. Oral manifestations in patients with coronavirus disease 2019 (COVID-19) identified using text mining: an observational study
Sandra Guauque-Olarte[SUP] 1 [/SUP], Laura Cifuentes-C[SUP] 2 [/SUP], Cristian Fong[SUP] 3 [/SUP]
Affiliations
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.
. 2023 Oct 18;13(1):17770.
doi: 10.1038/s41598-023-44784-2. Oral manifestations in patients with coronavirus disease 2019 (COVID-19) identified using text mining: an observational study
Sandra Guauque-Olarte[SUP] 1 [/SUP], Laura Cifuentes-C[SUP] 2 [/SUP], Cristian Fong[SUP] 3 [/SUP]
Affiliations
- PMID: 37853031
- DOI: 10.1038/s41598-023-44784-2
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.