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

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.

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

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.

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

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.

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

2021 ◽  
Vol 18 (15) ◽  
pp. 8231
Author(s):  
Chan Li ◽  
Zhaoya Liu ◽  
Ruizheng Shi
Keyword(s):  
Machine Learning ◽  
Myocardial Ischemia ◽  
Reperfusion Injury ◽  
Latent Dirichlet Allocation ◽  
Time Factors ◽  
Mesh Term ◽  
Myocardial Reperfusion ◽  
Myocardial Reperfusion Injury ◽  
Medical Subject Headings ◽  
The Past

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.

scholarly journals In defence of machine learning: Debunking the myths of artificial intelligence

2018 ◽  
Vol 14 (4) ◽  
pp. 734-747 ◽  
Author(s):  
Constance de Saint Laurent
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Progress ◽  
Life Time ◽  
Clear Understanding ◽  
Social Scientists ◽  
Scientific Publications ◽  
The Past ◽  
Potential Benefits ◽  
The Media

There has been much hype, over the past few years, about the recent progress of artificial intelligence (AI), especially through machine learning. If one is to believe many of the headlines that have proliferated in the media, as well as in an increasing number of scientific publications, it would seem that AI is now capable of creating and learning in ways that are starting to resemble what humans can do. And so that we should start to hope – or fear – that the creation of fully cognisant machine might be something we will witness in our life time. However, much of these beliefs are based on deep misconceptions about what AI can do, and how. In this paper, I start with a brief introduction to the principles of AI, machine learning, and neural networks, primarily intended for psychologists and social scientists, who often have much to contribute to the debates surrounding AI but lack a clear understanding of what it can currently do and how it works. I then debunk four common myths associated with AI: 1) it can create, 2) it can learn, 3) it is neutral and objective, and 4) it can solve ethically and/or culturally sensitive problems. In a third and last section, I argue that these misconceptions represent four main dangers: 1) avoiding debate, 2) naturalising our biases, 3) deresponsibilising creators and users, and 4) missing out some of the potential uses of machine learning. I finally conclude on the potential benefits of using machine learning in research, and thus on the need to defend machine learning without romanticising what it can actually do.

scholarly journals Analysis of the Trends in Biochemical Research Using Latent Dirichlet Allocation (LDA)

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 379 ◽  
Author(s):  
Kang ◽  
Kim ◽  
Kang
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Research Topics ◽  
Helpful Tool ◽  
Journal Editors ◽  
The Past ◽  
Mining Technique ◽  
Modeling Analysis ◽  
Funding Agencies ◽  
Biochemical Research

Biochemistry has been broadly defined as “chemistry of molecules included or related to living systems”, but is becoming increasingly hard to be distinguished from other related fields. Targets of its studies evolve rapidly; some newly emerge, disappear, combine, or resurface themselves with a fresh viewpoint. Methodologies for biochemistry have been extremely diversified, thanks particularly to those adopted from molecular biology, synthetic chemistry, and biophysics. Therefore, this paper adopts topic modeling, a text mining technique, to identify the research topics in the field of biochemistry over the past twenty years and quantitatively analyze the changes in its trends. The results of the topic modeling analysis obtained through this study will provide a helpful tool for researchers, journal editors, publishers, and funding agencies to understand the connections among the diverse sub-fields in biochemical research and even see how the research topics branch out and integrate with other fields.

scholarly journals Publication Landscape on Cushing's Disease: From the Past 40 Years to Future Directions

2021 ◽  
Author(s):  
Ming-Jie Luo ◽  
Kelei Du ◽  
Xiaopeng Guo ◽  
Zihao Wang ◽  
Bing Xing
Keyword(s):  
Clinical Trials ◽  
Cushing’S Disease ◽  
Latent Dirichlet Allocation ◽  
Basic Research ◽  
Research Topic ◽  
Cushing's Disease ◽  
International Studies ◽  
Future Directions ◽  
Country Level ◽  
Research Areas

Abstract PurposeThough literature related to Cushing's disease (CD) has grown significantly, previous reviews exclusively focused on specific research areas and were biased towards highly cited articles. This study aims to systemically analyze the research landscapes and trends using unbiased methods. MethodsWe queried all the CD-related publications in PubMed and clinical trials registered on clinicaltrials.gov. Latent Dirichlet allocation (LDA), a machine learning method, was used to derive research hotspots from article texts. The research topic clusters and country-level collaboration were revealed by network analysis.Results5015 articles were published since 1981, currently growing at 155 per year, with more retrospective studies but fewer prospective studies. Interestingly, the most popular LDA research topics were complications and comorbidities, endocrine hormone tests and surgical therapy, and they formed a remarkable triangle relationship in the research topic network. These topics had numerous international studies and were supported by most funding. In addition, many topics in the basic research domain were proliferating, including mutation, biomarkers, endopeptidases, and other molecular genetics and pathology of CD. Out of 63 registered clinical trials, over 25% were withdrawn due to inadequate patient recruitment or lack of funding.ConclusionsThis publication landscape analysis provided a systemic representation of CD literature regarding the history, current challenges, and future directions, enabling clinicians a rapid and comprehensive insight into the disease.

scholarly journals Smart literature review: a practical topic modelling approach to exploratory literature review

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Claus Boye Asmussen ◽  
Charles Møller
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Modelling ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Literature Reviews ◽  
Modelling Approach

Abstract Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.

3D Printing As a Significant Achievement for Application in Posttraumatic Surgeries - A Literature Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Michał Smoczok ◽  
Krzysztof Starszak ◽  
Weronika Starszak
Keyword(s):  
Clinical Trials ◽  
3D Printing ◽  
Mesh Term ◽  
Review Article ◽  
Trauma Patients ◽  
Scientific Publications ◽  
Imaging Diagnostics ◽  
Printing Technology ◽  
3D Printing Technology ◽  
Significant Achievement

Background: 3D printing is increasingly used in many fields of medicine. The broadening of knowledge in this field and the cooperation of doctors and engineers increases the interest in this technology and results in attempts to implement it at every stage of the treatment. Objective: The review aims to summarize the current literature on the use of 3D printing technology in the treatment of post trauma patients. Method: A review of available scientific publications in PubMed regarding 3D printing and its application in the context of posttraumatic procedures was carried out. Clinical Trials and Reviews from the period 2014-2019 (6-year period) were taken into consideration. The database was searched for "Printing", "ThreeDimensional" [MAJR] [MeSH Term]. Finally, 48 studies have been included in our review article. Results: 3D printing technology has a number of applications in patients who have suffered injuries. 3D printing has found application in preparation for procedures, accurate visualization of occurring injuries and complications, education of doctors and patients, prototyping, creation of synthetic scaffolding, production and implementation of target implants and rehabilitation. Conclusion: 3D printing is increasingly used in providing for the posttraumatic patients. It is necessary to conduct further research in this area and to provide development opportunities in regarding biopolymers and bioprinting. It is also necessary to improve cooperation between doctors and engineers and to create new centres that can comprehensively use 3D printing - from imaging diagnostics to the production of implants and their surgical use.

scholarly journals Rett Syndrome: Coming to Terms with Treatment

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Alan Percy
Keyword(s):  
Clinical Trials ◽  
Clinical Research ◽  
Rett Syndrome ◽  
Basic Science ◽  
Basic Research ◽  
The Past ◽  
Research Consortium ◽  
Science Investigations ◽  
Remarkable Progress ◽  
Important Progress

Rett syndrome (RTT) has experienced remarkable progress over the past three decades since emerging as a disorder of worldwide proportions, particularly with discovery of the linkage of RTT to MECP2 mutations. The advances in clinical research and the increasing pace of basic science investigations have accelerated the pattern of discovery and understanding. Clinical trials are ongoing and others are planned. A review of these events and the prospects for continued success are highlighted below. The girls and women encountered today with RTT are, overall, in better general, neurologic, and behavioral health than those encountered earlier. This represents important progress worldwide from the concerted efforts of a broadly based and diverse clinical and basic research consortium as well as the efforts of parents, family, and friends.

scholarly journals Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

2017 ◽  
Vol 5 ◽  
pp. 191-204 ◽  
Author(s):  
Jooyeon Kim ◽  
Dongwoo Kim ◽  
Alice Oh
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Scientific Progress ◽  
Joint Modeling ◽  
Research Topics ◽  
Scientific Publications ◽  
Generative Process ◽  
Author Citation ◽  
Improved Accuracy ◽  
Dirichlet Allocation

Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author’s influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI into four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.

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