A bibliometric analysis of worldwide cancer research using machine learning methods.

Lin, Lianghong; Liang, Likeng; Wang, Maojie; Huang, Runyue; Gong, Mengchun; Song, Guangjun; Hao, Tianyong

Lin, Lianghong; Liang, Likeng; Wang, Maojie; Huang, Runyue; Gong, Mengchun; Song, Guangjun; Hao, Tianyong.

Afiliação

Lin L; School of Artificial Intelligence South China Normal University Guangzhou China.
Liang L; School of Computer Science South China Normal University Guangzhou China.
Wang M; Guangdong Provincial Hospital of Chinese Medicine Guangzhou China.
Huang R; Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome Guangzhou China.
Gong M; State Key Laboratory of Dampness Syndrome of Chinese Medicine The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou China.
Song G; Guangdong Provincial Hospital of Chinese Medicine Guangzhou China.
Hao T; Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome Guangzhou China.

Cancer Innov ; 2(3): 219-232, 2023 Jun.

Article em En | MEDLINE | ID: mdl-38089405

Palavras-chave

Latent Dirichlet Allocation; bibliometric analysis; cancer; machine learning; research topic; topic evolution

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Cancer Innov Ano de publicação: 2023 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Cancer Innov Ano de publicação: 2023 Tipo de documento: Article