A Bibliometric Analysis of 14,822 Researches on Myocardial Reperfusion Injury by Machine Learning

Myocardial ischemia is the major cause of death worldwide, and reperfusion is the standard intervention for myocardial ischemia. However, reperfusion may cause additional damage, known as myocardial reperfusion injury, for which there is still no effective therapy. This study aims to analyze the landscape of researches concerning myocardial reperfusion injury over the past three decades by machine learning. PubMed was searched for publications from 1990 to 2020 indexed under the Medical Subject Headings (MeSH) term “myocardial reperfusion injury” on 13 April 2021. MeSH analysis and Latent Dirichlet allocation (LDA) analyses were applied to reveal research hotspots. In total, 14,822 publications were collected and analyzed in this study. MeSH analyses revealed that time factors and apoptosis were the leading terms of the pathogenesis and treatment of myocardial reperfusion injury, respectively. In LDA analyses, research topics were classified into three clusters. Complex correlations were observed between topics of different clusters, and the prognosis is the most concerned field of the researchers. In conclusion, the number of publications on myocardial reperfusion injury increases during the past three decades, which mainly focused on prognosis, mechanism, and treatment. Prognosis is the most concerned field, whereas studies on mechanism and treatment are relatively lacking.

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

Therapeutic Advances in Gastroenterology ◽

10.1177/1756284820934594 ◽

2020 ◽

Vol 13 ◽

pp. 175628482093459

Author(s):

Kangtao Wang ◽

Chenzhe Feng ◽

Ming Li ◽

Qian Pei ◽

Yuqiang Li ◽

...

Keyword(s):

Machine Learning ◽

Rectal Cancer ◽

Latent Dirichlet Allocation ◽

Semantic Analysis ◽

Basic Research ◽

Mesh Term ◽

Publication Date ◽

Medical Subject Headings ◽

The Past ◽

Mesh Terms

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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A Bibliometric Analysis of 34,692 Publications on Thyroid Cancer by Machine Learning: How Much Has Been Done in the Past Three Decades?

Frontiers in Oncology ◽

10.3389/fonc.2021.673733 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zeyu Zhang ◽

Lei Yao ◽

Wenlong Wang ◽

Bo Jiang ◽

Fada Xia ◽

...

Keyword(s):

Machine Learning ◽

Differential Diagnosis ◽

Thyroid Cancer ◽

Active Surveillance ◽

Latent Dirichlet Allocation ◽

Experimental Studies ◽

Genomic Analysis ◽

Mesh Term ◽

Scientific Publications ◽

The Past

IntroductionThyroid cancer (TC) is the most common neck malignancy. However, a large number of publications of TC have not been well summarized and discussed with more comprehensive methods. The purpose of this bibliometric study is to summarize scientific publications during the past three decades in the field of TC using a machine learning method.Material and MethodsScientific publications focusing on TC from 1990 to 2020 were searched in PubMed using the MeSH term “thyroid neoplasms”. Full associated data were downloaded in the format of PubMed, and extracted in the R platform. Latent Dirichlet allocation (LDA) was adopted to identify the research topics from the abstract of each publication using Python.ResultsA total of 34,692 publications related to TC from the last three decades were found and included in this study with an average of 1,119.1 publications per year. Clinical studies and experimental studies shared the most proportion of publications, while the proportion of clinical trials remained at a relatively small level (5.87% as the highest in 2004). Thyroidectomy was the lead MeSH term, followed by prognosis, differential diagnosis, and fine-needle biopsy. The LDA analyses showed the study topics were divided into four clusters, including treatment management, basic research, diagnosis research, epidemiology, and cancer risk. However, a relatively weak connection was shown between treatment managements and basic researches. Top 10 most cited publications in recent years particularly highlighted the applications of active surveillance in TC.ConclusionThyroidectomy, differential diagnosis, genomic analysis, active surveillance are the most concerning topics in TC researches. Although the BRAF-targeted therapy is under development with promising results, there is still an urgent need for conversions from basic studies to clinical practice.

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A Bibliometric Analysis of 8,276 Publications During the Past 25 Years on Cholangiocarcinoma by Machine Learning

Frontiers in Oncology ◽

10.3389/fonc.2021.687904 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zeyu Zhang ◽

Zhiming Wang ◽

Yun Huang

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Latent Dirichlet Allocation ◽

Basic Research ◽

Mesh Term ◽

General Interest ◽

Research Topics ◽

Scientific Publications ◽

The Past ◽

Basic Studies

IntroductionCholangiocarcinoma (CCA) is the second most common hepatic malignancy. Progress and developments have also been made in the field of CCA management along with increasing scientific publications during the past decades, which reflect topics of general interest and suggest the future direction of studies. The purpose of this bibliometric study is to summarize scientific publications during the past 25 years in the field of CCA using a machine learning method.Material and MethodsScientific publications focusing on CCA from 1995 to 2019 were searched in PubMed using the MeSH term “cholangiocarcinoma.” Full associated data were downloaded in the format of PubMed and extracted in the R platform. Latent Dirichlet allocation (LDA) was adopted to identify the research topics from the abstract of each publication using Python.ResultsA total of 8,276 publications related to CCA from the last 25 years were found and included in this study. The most type of publications remained little changed, while the proportion of clinical trials remained relatively low (7.24% as the highest) and, more significantly, with a further downward trend during the recent years (1.42% in 2019). Neoplasm staging, hepatectomy, and survival rate were the most concerning terms among those who are diagnosis-related, treatment-related, and prognosis-related. The LDA analyses showed chemotherapy, hepatectomy, and stent as the highly concerned research topics of CCA treatment. Meanwhile, conversions from basic studies to clinical therapies were suggested by a poor connection between clusters of treatment management and basic research.ConclusionThe number of publications of CCA has increased rapidly during the past 25 years. Survival analysis, differential diagnosis, and microRNA expression are the most concerned topics in CCA studies. Besides, there is an urgent need for high-quality clinical trials and conversions from basic studies to clinical therapies.

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