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 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 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 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 Evolution of knowledge on meniscal biomechanics: a 40 year perspective

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Amin Mohamadi ◽  
Kaveh Momenzadeh ◽  
Aidin Masoudi ◽  
Kempland C. Walley ◽  
Kenny Ierardi ◽  
...  
Keyword(s):  
Future Research ◽  
Fe Model ◽  
Cruciate Ligaments ◽  
Fe Modeling ◽  
Meniscal Tears ◽  
Medical Subject Headings ◽  
Model Studies ◽  
The Past ◽  
Mesh Terms ◽  
Kinematic Studies

Abstract Background Knowledge regarding the biomechanics of the meniscus has grown exponentially throughout the last four decades. Numerous studies have helped develop this knowledge, but these studies have varied widely in their approach to analyzing the meniscus. As one of the subcategories of mechanical phenomena Medical Subject Headings (MeSH) terms, mechanical stress was introduced in 1973. This study aims to provide an up-to-date chronological overview and highlights the evolutionary comprehension and understanding of meniscus biomechanics over the past forty years. Methods A literature review was conducted in April 2021 through PubMed. As a result, fifty-seven papers were chosen for this narrative review and divided into categories; Cadaveric, Finite element (FE) modeling, and Kinematic studies. Results Investigations in the 1970s and 1980s focused primarily on cadaveric biomechanics. These studies have generated the fundamental knowledge basis for the emergence of FE model studies in the 1990s. As FE model studies started to show comparable results to the gold standard cadaveric models in the 2000s, the need for understanding changes in tissue stress during various movements triggered the start of cadaveric and FE model studies on kinematics. Conclusion This study focuses on a chronological examination of studies on meniscus biomechanics in order to introduce concepts, theories, methods, and developments achieved over the past 40 years and also to identify the likely direction for future research. The biomechanics of intact meniscus and various types of meniscal tears has been broadly studied. Nevertheless, the biomechanics of meniscal tears, meniscectomy, or repairs in the knee with other concurrent problems such as torn cruciate ligaments or genu-valgum or genu-varum have not been extensively studied.

Open-Ended Questions

2020 ◽  
pp. 202-218
Author(s):  
Subhadra Dutta ◽  
Eric M. O’Rourke
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Written Language ◽  
Text Data ◽  
Employee Survey ◽  
Trade Offs ◽  
Word Relatedness ◽  
Survey Responses

Natural language processing (NLP) is the field of decoding human written language. This chapter responds to the growing interest in using machine learning–based NLP approaches for analyzing open-ended employee survey responses. These techniques address scalability and the ability to provide real-time insights to make qualitative data collection equally or more desirable in organizations. The chapter walks through the evolution of text analytics in industrial–organizational psychology and discusses relevant supervised and unsupervised machine learning NLP methods for survey text data, such as latent Dirichlet allocation, latent semantic analysis, sentiment analysis, word relatedness methods, and so on. The chapter also lays out preprocessing techniques and the trade-offs of growing NLP capabilities internally versus externally, points the readers to available resources, and ends with discussing implications and future directions of these approaches.

The Most Cited Mesh Terms and Authors who Published Papers in Pubmed Central on the Topic of Medicine and Health Using Bibliometric Analyses

2019 ◽  
pp. 1-9
Author(s):  
Tsair-Wei Chien ◽  
Hing-Man Wu ◽  
Hsien-Yi Wang ◽  
Willy Chou
Keyword(s):  
Mesh Term ◽  
Deep Insight ◽  
Medical Subject Headings ◽  
Microsoft Excel ◽  
Pubmed Central ◽  
Current State ◽  
Mesh Terms ◽  
Control Gain ◽  
State Of Research ◽  
Insight Into

Aims: We visualized the current state of research on publication outputs and citations in the field of medicine and health to uncover topic burst and citations among medical subject headings (MeSH) clusters. Study Design: A bibliometric analysis. Place and duration of Study: Using Pubmed indexed articles to inspect the characteristics of topics on medicine and health since 1969. Methodology: Selecting 156 abstracts, author names, countries, and MeSH terms on January 10, 2019, from Pubmed Central (PMC) based on the terms of medicine and health in the title since 1969, we applied the x-index and impact factor to evaluate author individual research achievements and compute MeSH bibliometric performances. The bootstrapping method was used to estimate the median and its 95% confidence intervals and make differences in metrics among MeSH clusters. The dominant nations were selected using the x-index to display on a dashboard. We programmed Microsoft Excel VBA routines to extract data. Google Maps and Pajek software were used for displaying graphical representations. Results: We found that (1)the dominant countries/areas are the Unlited States, Taiwan, and Australia; (2) the author Grajales, Francisco Jose 3rd form Canada has the most cited metrics such as author IF=39.46 and x-index=6.28; (3)the MeSH terms of organization & administration, standards, and prevention & control gain the top three degree centralities among MeSH clusters; (4) No any differences in metrics were found among MeSH clusters; (5) the article(PMID= 24518354) with three MeSH term of delivery of health care, social media, and software and published in 2014 was cited most at least 62 times. Conclusion: Social network analysis provides wide and deep insight into the relationships among MeSH terms. The MeSH weighted scheme and x-index were recommended to academics for computing MeSH citations in the future.

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.

scholarly journals Human Breathomics Database

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Tien-Chueh Kuo ◽  
Cheng-En Tan ◽  
San-Yuan Wang ◽  
Olivia A Lin ◽  
Bo-Han Su ◽  
...  
Keyword(s):  
Text Mining ◽  
Exhaled Breath Condensate ◽  
Mesh Term ◽  
Exhaled Breath ◽  
Medical Subject Headings ◽  
Mesh Terms ◽  
Disease Information ◽  
Volatile Organic ◽  
Breath Condensate ◽  
Automatic Pipeline

Abstract Breathomics is a special branch of metabolomics that quantifies volatile organic compounds (VOCs) from collected exhaled breath samples. Understanding how breath molecules are related to diseases, mechanisms and pathways identified from experimental analytical measurements is challenging due to the lack of an organized resource describing breath molecules, related references and biomedical information embedded in the literature. To provide breath VOCs, related references and biomedical information, we aim to organize a database composed of manually curated information and automatically extracted biomedical information. First, VOCs-related disease information was manually organized from 207 literature linked to 99 VOCs and known Medical Subject Headings (MeSH) terms. Then an automated text mining algorithm was used to extract biomedical information from this literature. In the end, the manually curated information and auto-extracted biomedical information was combined to form a breath molecule database—the Human Breathomics Database (HBDB). We first manually curated and organized disease information including MeSH term from 207 literatures associated with 99 VOCs. Then, an automatic pipeline of text mining approach was used to collect 2766 literatures and extract biomedical information from breath researches. We combined curated information with automatically extracted biomedical information to assemble a breath molecule database, the HBDB. The HBDB is a database that includes references, VOCs and diseases associated with human breathomics. Most of these VOCs were detected in human breath samples or exhaled breath condensate samples. So far, the database contains a total of 913 VOCs in relation to human exhaled breath researches reported in 2766 publications. The HBDB is the most comprehensive HBDB of VOCs in human exhaled breath to date. It is a useful and organized resource for researchers and clinicians to identify and further investigate potential biomarkers from the breath of patients. Database URL: https://hbdb.cmdm.tw

scholarly journals Using MeSH (Medical Subject Headings) to Enhance PubMed Search Strategies for Evidence-Based Practice in Physical Therapy

2012 ◽  
Vol 92 (1) ◽  
pp. 124-132 ◽  
Author(s):  
Randy R. Richter ◽  
Tricia M. Austin
Keyword(s):  
Search Strategy ◽  
Evidence Based Practice ◽  
Physical Therapists ◽  
Mesh Term ◽  
Evidence Based ◽  
Medical Subject Headings ◽  
Search Terms ◽  
Mesh Terms ◽  
Subject Headings ◽  
Pubmed Search

Background Evidence-based practice (EBP) is an important paradigm in health care. Physical therapists report lack of knowledge and time constraints as barriers to EBP. Objective The purpose of this technical report is to illustrate how Medical Subject Headings (MeSH), a controlled vocabulary thesaurus of indexing terms, is used to efficiently search MEDLINE, the largest component of PubMed. Using clinical questions, this report illustrates how search terms common to physical therapist practice do or do not map to appropriate MeSH terms. A PubMed search strategy that takes advantage of text words and MeSH terms is provided. Results A search of 139 terms and 13 acronyms was conducted to determine whether they appropriately mapped to a MeSH term. The search results were categorized into 1 of 5 outcomes. Nearly half (66/139) of the search terms mapped to an appropriate MeSH term (outcome 1). When a search term did not appropriately map to a MeSH term, it was entered into the MeSH database to search for an appropriate MeSH term. Twenty-one appropriate MeSH terms were found (outcomes 2 and 4), and there were 52 search terms for which an appropriate MeSH term was not found (outcomes 3 and 5). Nearly half of the acronyms did not map to an appropriate MeSH term, and an appropriate MeSH term was not found in the database. Limitations The results are based on a limited number of search terms and acronyms. Conclusions Understanding how search terms map to MeSH terms and using the PubMed search strategy can enable physical therapists to take full advantage of available MeSH terms and should result in more-efficient and better-informed searches.

The Role of Technology and Informatics in Disaster Planning and Response

2012 ◽  
Vol 30 (1) ◽  
pp. 149-168 ◽  
Author(s):  
Elizabeth Weiner ◽  
Lynn A. Slepski
Keyword(s):  
Disaster Planning ◽  
Mesh Term ◽  
Disaster Medicine ◽  
Medical Subject Headings ◽  
Humanitarian Response ◽  
Mesh Terms ◽  
Pubmed Search ◽  
Impact Of Technology ◽  
The Impact

It is clear that technology and informatics are becoming increasingly important in disasters and humanitarian response. Technology is a critical tool to recording, analyzing, and predicting trends in data that could not be achieved prior to its implementation. Informatics is the translation of this data into information, knowledge, and wisdom. Combining technology and informatics applications with response efforts has resulted in various enhanced biosurveillance efforts, advanced communications, and information management during disasters. Although these efforts have been well described in the literature, research on the impact of technology and informatics during these efforts has been limited. As a result, this chapter will provide an overview of these technology and informatics solutions and present suggestions for further research in an era when disaster and humanitarian response efforts continue to increase as well. A literature search was performed using PubMed search tools with the National Library of Medicine Medical Subject Headings (MeSH) terms of “disasters,” “disaster planning,” “disaster medicine,” “technology,” “informatics,” and “research.” Search limitations were set for 5 years and in English. Because of the limited number of research articles in this field, the MeSH term research was deleted.

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