A bibliometric analysis and visualization of medical data mining research

Robotic exoskeleton-assisted walking rehabilitation for stroke patients: a bibliometric and visual analysis

Objective

This study aimed to conduct a bibliometric analysis of the literature on exoskeleton robot assisted walking rehabilitation for stroke patients in the Web of Science Core Collection over the past decade.

Method

Retrieved literature on exoskeleton robot assisted gait training for stroke hemiplegic patients from the Web of Science Core Collection from 1 January 2014 to 31 January 2024. The search method was topic search, and the types of documents were "article, meeting abstract, review article, early access." CiteSpace was used to analyze the search results from countries, institutions, keywords, cited references and cited authors.

Result

A total of 1,349 articles were retrieved, and 1,034 were ultimately included for visualization analysis. The annual publication volume showed an upward trend, with countries, institutions, and authors from Europe and America in a leading position. The core literature was also published by authors from European and American countries. The keywords were divided into 8 clusters: # 0 soft robotic exit, # 1 robot assisted gain training, # 2 multiple scales, # 3 magnetic rheological brake, # 4 test retest reliability, # 5 electromechanical assisted training, # 6 cerebra salary, and # 7 slow gain. The early research direction focused on the development of exoskeleton robots, verifying their reliability and feasibility. Later, the focus was on the combination of exoskeleton robot with machine learning and other technologies, rehabilitation costs, and patient quality of life.

Conclusion

This study provides a visual display of the research status, development trends, and research hotspots, which helps researchers in this field to grasp the research hotspots and choose future research directions.

Emerging trends and patterns in healthcare-seeking behavior: A systematic review

OBJECTIVES

The study of healthcare-seeking behavior is essential for optimizing resource allocation and improving healthcare services. Its complexity and diversity have made it a prominent research area. Understanding factors influencing healthcare-seeking decisions allows targeted interventions and policy development to address barriers and ensure equitable access to quality healthcare for diverse populations. Such research plays a vital role in enhancing healthcare outcomes and overall population health.

METHODS

The study utilized a systematic quantitative literature review approach, employing the Web of Science (WOS) Core Collection and PubMed databases as data sources. Additionally, bibliometric tools such as CiteSpace and VOSviewer were employed for analysis and visualization of the literature.

RESULTS

A comprehensive statistical analysis and visualization were performed on the annual publication volume, publication countries, journals, keywords, and keyword co-occurrence patterns up until 2023. Through this analysis, a framework was established, identifying the determinants and fundamental elements of healthcare-seeking behavior. These findings contribute to the advancement of research in this field and inform future studies and interventions aimed at improving healthcare-seeking behavior.

CONCLUSIONS

Based on the aforementioned literature review and framework, several conclusions were drawn. The determinants that facilitate healthcare-seeking behavior include improving health education awareness, enhancing healthcare resources, reducing costs, and ensuring system soundness. Additionally, providing social environment support was found to be crucial. Furthermore, the fundamental elements of healthcare-seeking behavior were identified as healthcare demand, healthcare choices, and the process of diagnosis and treatment. These findings provide valuable insights for developing interventions and policies to promote optimal healthcare-seeking behavior.

Trends in photodynamic therapy for dermatology in recent 20 years: A scientometric review based on CiteSpace

OBJECTIVE

Analyze the research state and development trend of photodynamic therapy for dermatology using visual knowledge graphs derived from the Web of Science Core Collection database.

METHODS

The Web of Science Core Collection database was utilized as the search data source for the bibliometric analysis, and the associated articles published between January 1, 2000, and December 31, 2022, were obtained using the search terms "photodynamic therapy" and "dermatology". CiteSpace, VOSviewer, and additional tools were utilized for bibliometric analysis, and visual knowledge graphs were created.

RESULTS

Eight hundred and thirty two articles were retrieved in total, and 747 were included following de-duplication and transformation. The country with the greatest number of publications is the United States; the primary research institution was University of Copenhagen; and the references with the highest centrality were primarily concerned with the selection of photosensitizers; High frequency keywords primarily comprised 5 aminolevulinic acid and basal cell carcinoma; and the clustering graph revealed that all keywords fell into 11 categories.

CONCLUSION

In numerous areas of dermatology, photodynamic treatment is commonly employed. Current research focuses on nonneoplastic skin diseases and the choice of photosensitizers. Nonetheless, its specific mechanism and other applications merit further investigation.

Bibliometric structured review of tuberculosis in Nigeria

Background: The tuberculosis burden is growing in Nigeria along with its population. For example, Nigeria has the sixth highest TB burden globally, with an estimated 4.3 per cent multi-drug resistance in new cases. This study builds on the existing study that examined academic involvement in tuberculosis research. The study in question focused on global medical literature related to tuberculosis, but the non-visibility of some low and middle-income countries in the bigger global picture motivated this present study. Every year, over 245,000 Nigerians succumb to tuberculosis (TB), with approximately 590,000 new cases reported (of these, around 140,000 are also HIV-positive). This study carried out an academic publication evaluation with the VOS viewer tool to map bibliometric data for scholarly articles published between 1991 and 2021 on tuberculosis research and used the Biblioshiny app for analytics and plots of authors, sources, and documents to explore the descriptive statistics of tuberculosis literature. The present study delineates that England has the highest collaborating country with Nigeria in the study of tuberculosis over the years and according to the report, the University of Nigeria, the University of Ibadan, and Nnamdi Azikwe University are Nigerian institutions with extensive collaborations. This study concludes with managerial implications for future actions.

[One hundred years of the Pan American Journal of Public Health: visualizing its contentCem anos da Revista Pan-Americana de Saúde Pública: visualização do seu conteúdo]

Objectives

Explore how the content of the articles published by the Pan American Journal of Public Health during its 100 years of existence has aligned with the key health issues of the Pan American Health Organization (PAHO).

Methods

A bibliometric analysis was carried out, with visualization of its results. Information on articles published in the Journal was retrieved from PAHO's Institutional Repository for Information Sharing (IRIS) for the first 75 years and Scopus for the last 25 years, until February 2022; References to Governing Bodies documents and statements by the directors were used to establish PAHO's key themes.

Results

Initially, 12 573 publications were obtained and 9 289 were considered for analysis for the period 1922 to 1996, and 3 208 for the period 1997 to 2022. For the bibliometric analysis of the Scopus information, indicators such as the authors and their origin, language of publication, and number and origin of citations were considered. For the visualizations, publications were divided into five periods so that they coincided with the periods established for the analysis of PAHO's priority themes. Keyword co-occurrence maps were made to observe the evolution of published topics and relate them to public health approaches in each period.

Conclusion

The topics published in the Pan American Journal of Public Health and its precursor bulletins reflect the history of regional public health and its evolution over time, as well as the key health issues of the Pan American Health Organization.

Decoding Diabetes Biomarkers and Related Molecular Mechanisms by Using Machine Learning, Text Mining, and Gene Expression Analysis

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.

A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

PURPOSE

Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation.

METHODS

This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates.

RESULTS

The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers.

CONCLUSIONS

Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.

Prediction of the Molecular Mechanism of Eucommiae Cortex - Achyranthis Bidentatae Radix in the treatment of Osteoarthritis: Network Pharmacology and Molecular Docking

OBJECTIVE

To retrieve the core drug of osteoarthritis in clinic using Data Mining, predict the drug molecular action target through the Network Pharmacology, identify the key nodes of the interaction by combining with the related targtes of osteoarthritis, explore the pharmacological mechanism of Traditional Chinese Medicine against osteoarthritis and other possible mechanisms of actions.

METHODS

to retrieve the commonly used therapeutic formulations for osteoarthritis patients in clinical with PubMed, CNKI, VIP, CBM, WanFang Database and other databases, and screen out the core drugs through the Ancient and Modern Medical Case Cloud Platform and software Gephi, filter out the core drug molecules and targets combined with TCMSP database and the targets of osteoarthritis in Genecard and OMIM database, plunge those data into R project and Cytoscape to construct the intersection model of Drug molecule-osteoarthritis, establish PPI network and GO and conduct KEGG enrichment analysis with String database. Vina molecular docking was finally implemented to draw molecular docking diagram, and the results were analyzed after comprehensive analysis.

RESULTS

The core drug pairs were identified as "Eucommiae Cortex - Achyranthis Bidentatae Radix" through correlation analysis, complex network analysis based on the coefficient. "Eucommiae Cortex - Achyranthis Bidentatae Radix" can intervene cell behavior through multiple pathways and regulate cell metabolism, cytokine synthesis, oxidative and cellular immunity with the help of topology analysis in String Database.

CONCLUSIONS

The core molecules of Quercetin and Kaempferol derived from "Eucommia bark - achyranthes" can change the spatial conformation of PTGSs by hydrogen bonding with PTGSs, the hydrophobic bonds and van der Waals forces generated by Baicalein, Wogonin and β-carotene, thereby changing the activity of PTGSs and affecting bone properties the process of osteoarthritis.

Evolving Trends and Research Hotspots in Disaster Epidemiology From 1985 to 2020: A Bibliometric Analysis

Background: Disaster epidemiology has not attracted enough attention in the past few decades and still faces significant challenges. This study aimed to systematically analyze the evolving trends and research hotspots in disaster epidemiology and provide insights into disaster epidemiology.

Methods: We searched the Scopus and Web of Science Core Collection (WoSCC) databases between 1985 and 2020 to identify relevant literature on disaster epidemiology. The retrieval strategies were TITLE-ABS-KEY (disaster epidemiology) and TS = (disaster AND epidemiology). Bibliometrix, VOSviewer 1.6.6 and SigmaPlot 12.5 were used to analyze the key bibliometric indicators, including trends and annual publications, the contributions of countries, institutions, journals and authors, and research hotspots.

Results: A total of 1,975 publications were included. There was an increasing trend in publications over the past 35 years. The USA was the most productive country. The most frequent institutions and journals were Fukushima Medical University and Prehospital and Disaster Medicine. Galea S made significant contributions to this field. “Epidemiology” was the highest-frequency keyword. COVID-19 was highly cited after 2019. Three research hotspots were identified: (i) the short- and long-term adverse health effects of disasters on the population; (ii) COVID-19 pandemic and emergency preparedness; and (iii) disaster management.

Conclusions: In recent decades, the USA was a global leader in disaster epidemiology. Disaster management, the short- and long-term health effects of disasters, and the COVID-19 pandemic reflected the research focuses. Our results suggest that these directions will remain research hotspots in the future. International collaboration is also expected to widen and deepen in the field of disaster epidemiology.

Trends of global health literacy research (1995-2020): Analysis of mapping knowledge domains based on citation data mining

Background

During uncertainties associated with the COVID-19 pandemic, effectively improving people’s health literacy is more important than ever. Drawing knowledge maps of health literacy research through data mining and visualized measurement technology helps systematically present the research status and development trends in global academic circles.

Methods

This paper uses CiteSpace to carry out a metric analysis of 9,492 health literacy papers included in Web of Science through mapping knowledge domains. First, based on the production theory of scientific knowledge and the data mining of citations, the main bodies (country, institution and author) that produce health literacy knowledge as well as their mutual cooperation (collaboration network) are both clarified. Additionally, based on the quantitative framework of cocitation analysis, this paper introduces the interdisciplinary features, development trends and hot topics of the field. Finally, by using burst detection technology in the literature, it further reveals the research frontiers of health literacy.

Results

The results of the BC measures of the global health literacy research collaboration network show that the United States, Australia and the United Kingdom are the major forces in the current international collaboration network on health literacy. There are still relatively very few transnational collaborations between Eastern and Western research institutions. Collaborations in public environmental occupational health, health care science services, nursing and health policy services have been active in the past five years. Research topics in health literacy research evolve over time, mental health has been the most active research field in recent years.

Conclusions

A systematic approach is needed to address the challenges of health literacy, and the network framework of cooperation on health literacy at regional, national and global levels should be strengthened to further promote the application of health literacy research. In the future, we anticipate that this research field will expand in two directions, namely, mental health literacy and eHealth literacy, both of which are closely linked to social development and issues. The results of this study provide references for future applied research in health literacy.

Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.