Visualization studies on evidence-based medicine domain knowledge (series 2): structural diagrams of author networks (original) (raw)
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2021
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Knowledge Graph Modeling in Healthcare: A Bibliometric Analysis
Jurnal Komputer Terapan
Healthcare issues are currently the most researched issues worldwide. Many healthcare researchers collaborate with non-healthcare researchers to improve the quality of healthcare. The knowledge graph is a widely used computer science and mathematics approach to solve healthcare issues. It can model the relationship between events to build new knowledge. Hence, a comprehensive study on knowledge graph modeling in healthcare was conducted in this study. The research methodologies in this study were: (1) article retrieval and general bibliometric analysis; (2) visualization of research distribution; and (3) research recommendations. In the last three years, 867 articles were retrieved from three databases. The citation metrics analysis was also conducted to determine the quality level of articles retrieval. An analysis was conducted using network and density visualization related to the relationship between research topics and trends. The final results in this paper are recommendations...
Graph-based Tool for Exploring PubMed Knowledge Base
Studies have shown that data retrieval and visualization tools can help health professionals to improve their understanding and communication with patients, their relationship with stakeholders, and their decision-making process. However, not many efforts have been made in this direction. In this paper, we present a prototype system for the indexing, annotation, and visualization of the PubMed knowledge base to enable the search and retrieval of health-related evidence. The proposed tool builds and keeps updated an enriched graph based on PubMed articles associating them with concepts extracted from the Unified Medical Language System (UMLS) Metathesaurus. Moreover, it allows a full-text search and graph-based navigation and supports an overview of concepts and related publications. The proposed architecture enables scale-up thanks to its containerized nature and parallelization capabilities. The code is open-source under the Apache V2 license.
MET network in PubMed: a text-mined network visualization and curation system
Database, 2016
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Journal of Diabetes & Metabolic Disorders, 2020
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Visualization of the Scholarly Output on Evidence Based Librarianship: A Social Network Analysis
Evidence Based Library and Information Practice
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2005
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Introduction: We aim to visualize and analyze the science map of Cochrane systematic reviews with the high altmetric attention scores. Methods: On 10 May 2019, altmetric data of Cochrane Database of Systematic Reviews obtained from Altmetric database (Altmetric LLP, London, UK). Bibliometric data of top 5% Cochrane systematic reviews further extracted from Web of Science. keyword co-occurrence, co-authorship and co-citation network visualization were then employed using VOSviewer software. Decision tree and random forest model were used to analyze citations pattern. Results: 12016 Cochrane systematic reviews with Altmetric attention are found (total mentions=259,968). Twitter was the most popular altmetric resource among these articles. Consequently, the top 5% (607 articles, mean altmetric score= 171.2, Confidence Level (CL) 95%= 14.4, mean citations= 42.1, CL 95%= 1.3) with the highest Altmetric score are included in the study. Keyword co-occurrence network visualization showed fe...
Visualization of e-Health Research Topics and Current Trends Using Social Network Analysis
Telemedicine journal and e-health : the official journal of the American Telemedicine Association, 2015
E-health has been grown rapidly with significant impact on quality and safety of healthcare. However, there is a large gap between the postulated and empirically demonstrated benefits of e-health technologies and a need for a clearer mapping of its conceptual domains. Therefore, this study aimed to critically review the main research topics and trends of international e-health through social network analysis. Medical subject heading terms were used to retrieve 3,023 research articles published from 1979 through 2014 in the PubMed database. We extracted n-grams from the corpus using a text analysis program, generated co-occurrence networks, and then analyzed and visualized the networks using Pajek software. The hub and authority measures identified the most important research topics in e-health. Newly emerging topics by 4-year period units were identified as research trends. The most important research topics in e-health are personal health records (PHR), health information technolog...
An automatic method to generate domain-specific investigator networks using PubMed abstracts
2007
Background: Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. Results: We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit) as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8%) and from 94.2% of HuGE PubMed records (accuracy 87.0). We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit), indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70-90% of the investigators/ collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. Conclusion: We successfully created a web-based prototype capable of creating domain-specific investigator networks based on an application that accurately generates detailed investigator profiles from PubMed abstracts combined with robust standard vocabularies. This approach could be used for other biomedical fields to efficiently establish domain-specific investigator networks.