A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed.

ABSTRACT:

Background and aims

The aim of this study was to analyse the landscape of publications on rectal cancer (RC) over the past 25 years by machine learning and semantic analysis.

Methods

Publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Rectal Neoplasms' from 1994 to 2018 were downloaded in September 2019. R and Python were used to extract publication date, MeSH terms and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation was applied to analyse the text from the articles' abstracts to identify more specific research topics. Louvain algorithm was used to establish a topic network resulting in identifying the relationship between the topics.

Results

A total of 23,492 papers published were iden

SUBMITTER: Wang K 

PROVIDER: S-EPMC7385823 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature