Cargando…

Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years

Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) te...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Kangtao, Herr, Ingrid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995465/
https://www.ncbi.nlm.nih.gov/pubmed/35419289
http://dx.doi.org/10.3389/fonc.2022.832385
_version_ 1784684303368585216
author Wang, Kangtao
Herr, Ingrid
author_facet Wang, Kangtao
Herr, Ingrid
author_sort Wang, Kangtao
collection PubMed
description Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term ‘Pancreatic Neoplasms’ from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication’s metadata for bibliometric analyses. The generative statistical algorithm “latent Dirichlet allocation” (LDA) was applied to identify specific research topics and trends. The unsupervised “Louvain algorithm” was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term “pancreatic cancer research”, 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics “Clinical Manifestation and Diagnosis”, “Review and Management”, “Treatment Studies”, and “Basic Research”. The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that “Immunotherapy”, Prognostic research”, “Protein expression”, “Case reports”, “Gemcitabine and mechanism”, “Clinical study of gemcitabine”, “Operation and postoperation”, “Chemotherapy and resection”, and “Review and management” as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware.
format Online
Article
Text
id pubmed-8995465
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-89954652022-04-12 Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years Wang, Kangtao Herr, Ingrid Front Oncol Oncology Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term ‘Pancreatic Neoplasms’ from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication’s metadata for bibliometric analyses. The generative statistical algorithm “latent Dirichlet allocation” (LDA) was applied to identify specific research topics and trends. The unsupervised “Louvain algorithm” was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term “pancreatic cancer research”, 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics “Clinical Manifestation and Diagnosis”, “Review and Management”, “Treatment Studies”, and “Basic Research”. The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that “Immunotherapy”, Prognostic research”, “Protein expression”, “Case reports”, “Gemcitabine and mechanism”, “Clinical study of gemcitabine”, “Operation and postoperation”, “Chemotherapy and resection”, and “Review and management” as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware. Frontiers Media S.A. 2022-03-28 /pmc/articles/PMC8995465/ /pubmed/35419289 http://dx.doi.org/10.3389/fonc.2022.832385 Text en Copyright © 2022 Wang and Herr https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Wang, Kangtao
Herr, Ingrid
Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years
title Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years
title_full Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years
title_fullStr Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years
title_full_unstemmed Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years
title_short Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years
title_sort machine-learning-based bibliometric analysis of pancreatic cancer research over the past 25 years
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995465/
https://www.ncbi.nlm.nih.gov/pubmed/35419289
http://dx.doi.org/10.3389/fonc.2022.832385
work_keys_str_mv AT wangkangtao machinelearningbasedbibliometricanalysisofpancreaticcancerresearchoverthepast25years
AT herringrid machinelearningbasedbibliometricanalysisofpancreaticcancerresearchoverthepast25years