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

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...

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Detalles Bibliográficos
Autores principales: Wang, Kangtao, Feng, Chenzhe, Li, Ming, Pei, Qian, Li, Yuqiang, Zhu, Hong, Song, Xiangping, Pei, Haiping, Tan, Fengbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385823/
https://www.ncbi.nlm.nih.gov/pubmed/32782478
http://dx.doi.org/10.1177/1756284820934594
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author Wang, Kangtao
Feng, Chenzhe
Li, Ming
Pei, Qian
Li, Yuqiang
Zhu, Hong
Song, Xiangping
Pei, Haiping
Tan, Fengbo
author_facet Wang, Kangtao
Feng, Chenzhe
Li, Ming
Pei, Qian
Li, Yuqiang
Zhu, Hong
Song, Xiangping
Pei, Haiping
Tan, Fengbo
author_sort Wang, Kangtao
collection PubMed
description 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 identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies. CONCLUSIONS: The number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future.
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spelling pubmed-73858232020-08-10 A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed Wang, Kangtao Feng, Chenzhe Li, Ming Pei, Qian Li, Yuqiang Zhu, Hong Song, Xiangping Pei, Haiping Tan, Fengbo Therap Adv Gastroenterol Advances and Future Perspectives in Colorectal Cancer 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 identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies. CONCLUSIONS: The number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future. SAGE Publications 2020-07-27 /pmc/articles/PMC7385823/ /pubmed/32782478 http://dx.doi.org/10.1177/1756284820934594 Text en © The Author(s), 2020 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Advances and Future Perspectives in Colorectal Cancer
Wang, Kangtao
Feng, Chenzhe
Li, Ming
Pei, Qian
Li, Yuqiang
Zhu, Hong
Song, Xiangping
Pei, Haiping
Tan, Fengbo
A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed
title A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed
title_full A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed
title_fullStr A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed
title_full_unstemmed A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed
title_short A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed
title_sort bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed
topic Advances and Future Perspectives in Colorectal Cancer
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385823/
https://www.ncbi.nlm.nih.gov/pubmed/32782478
http://dx.doi.org/10.1177/1756284820934594
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