Text mining tweets on e-cigarette risks and benefits using machine learning following a vaping related lung injury outbreak in the USA.

Hassan, Lamiece; Elkaref, Mohab; de Mel, Geeth; Bogdanovica, Ilze; Nenadic, Goran

Hassan, Lamiece; Elkaref, Mohab; de Mel, Geeth; Bogdanovica, Ilze; Nenadic, Goran.

Afiliación

Hassan L; Division of Informatics, Imaging and Data Sciences, The University of Manchester, UK.
Elkaref M; IBM Research, Daresbury, UK.
de Mel G; IBM Research, Daresbury, UK.
Bogdanovica I; School of Medicine, University of Nottingham, UK.
Nenadic G; School of Computer Science, The University of Manchester, UK.

Healthc Anal (N Y) ; 2: None, 2022 Nov.

Article en En | MEDLINE | ID: mdl-36605918

Palabras clave

ENDS; ENDS, Electronic nicotine delivery systems; EVALI; EVALI, E-cigarette or Vaping product use Associated Lung Injury; Machine learning; Public health; Social media; THC, tetrahydrocannabinol; Twitter; UK; USA; e-cigarettes

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Qualitative_research / Risk_factors_studies Idioma: En Revista: Healthc Anal (N Y) Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google